Insert Abstract
The following document contains results from all analyses conducted for the manuscript titled “Trajectories of Risk-taking Propensity: A Coordinated Analysis of Longitudinal Panels”. This document is organized by different domain risk-taking propensity,including general, driving, financial, recreational, occupational, health and social domain. For each risk-taking propensity, we create 7 models (including intercept-only model, fixed effect model, linear model, linear with gender model, linear with gender interaction model, quadratic model and quadratic with gender model) and provide a table summarizing individual study model results, trajectory plots, the meta-analysis results. We tested individual predictors that are not included in the simple trajectory model in meta regression: country, continent, mean age and scale range. And the results from these models are available below. The code used to compile this file is available here (insert link)
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.9766 -7.9532 -3.9532 -4.3697 0.0468
##
## tau^2 (estimated amount of total heterogeneity): 0.0155 (SE = 0.0090)
## tau (square root of estimated tau^2 value): 0.1246
## I^2 (total heterogeneity / total variability): 99.83%
## H^2 (total variability / sampling variability): 582.81
##
## Test for Heterogeneity:
## Q(df = 6) = 3633.8026, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4330 0.0472 9.1660 <.0001 0.3404 0.5256 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9112 -5.8225 2.1775 -0.2773 42.1775
##
## tau^2 (estimated amount of residual heterogeneity): 0.0136 (SE = 0.0097)
## tau (square root of estimated tau^2 value): 0.1166
## I^2 (residual heterogeneity / unaccounted variability): 99.68%
## H^2 (unaccounted variability / sampling variability): 316.66
## R^2 (amount of heterogeneity accounted for): 12.31%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 1457.3211, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.8299, p-val = 0.2429
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3250 0.0826 3.9351 <.0001 0.1631 0.4868 ***
## continentEurope 0.1798 0.1069 1.6821 0.0926 -0.0297 0.3892 .
## continentNorth America 0.1096 0.1168 0.9383 0.3481 -0.1193 0.3385
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6056 -7.2112 -1.2112 -2.3829 22.7888
##
## tau^2 (estimated amount of residual heterogeneity): 0.0138 (SE = 0.0088)
## tau (square root of estimated tau^2 value): 0.1175
## I^2 (residual heterogeneity / unaccounted variability): 99.80%
## H^2 (unaccounted variability / sampling variability): 490.35
## R^2 (amount of heterogeneity accounted for): 11.08%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 3487.6720, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.7437, p-val = 0.1867
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0226 0.3139 0.0721 0.9425 -0.5926 0.6379
## mean.age 0.0080 0.0061 1.3205 0.1867 -0.0039 0.0199
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.9766 -7.9532 -3.9532 -4.3697 0.0468
##
## tau^2 (estimated amount of total heterogeneity): 0.0155 (SE = 0.0090)
## tau (square root of estimated tau^2 value): 0.1246
## I^2 (total heterogeneity / total variability): 99.83%
## H^2 (total variability / sampling variability): 582.81
##
## Test for Heterogeneity:
## Q(df = 6) = 3633.8026, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4330 0.0472 9.1660 <.0001 0.3404 0.5256 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.5846 -23.1692 -19.1692 -19.5857 -15.1692
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0321
## I^2 (total heterogeneity / total variability): 97.81%
## H^2 (total variability / sampling variability): 45.72
##
## Test for Heterogeneity:
## Q(df = 6) = 209.9416, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0767 0.0126 -6.1045 <.0001 -0.1014 -0.0521 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.4638 -14.9277 -6.9277 -9.3825 33.0723
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0349
## I^2 (residual heterogeneity / unaccounted variability): 96.97%
## H^2 (unaccounted variability / sampling variability): 32.99
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 109.9605, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.4546, p-val = 0.4832
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0837 0.0253 -3.3138 0.0009 -0.1332 -0.0342
## continentEurope -0.0066 0.0330 -0.1998 0.8416 -0.0712 0.0580
## continentNorth America 0.0316 0.0356 0.8892 0.3739 -0.0381 0.1013
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.3843 -18.7685 -12.7685 -13.9402 11.2315
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0341
## I^2 (residual heterogeneity / unaccounted variability): 98.09%
## H^2 (unaccounted variability / sampling variability): 52.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 195.1708, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5208, p-val = 0.4705
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1430 0.0925 -1.5464 0.1220 -0.3243 0.0382
## mean.age 0.0013 0.0018 0.7217 0.4705 -0.0022 0.0048
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.5846 -23.1692 -19.1692 -19.5857 -15.1692
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0321
## I^2 (total heterogeneity / total variability): 97.81%
## H^2 (total variability / sampling variability): 45.72
##
## Test for Heterogeneity:
## Q(df = 6) = 209.9416, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0767 0.0126 -6.1045 <.0001 -0.1014 -0.0521 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.5819 -23.1638 -19.1638 -19.5803 -15.1638
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0321
## I^2 (total heterogeneity / total variability): 97.75%
## H^2 (total variability / sampling variability): 44.45
##
## Test for Heterogeneity:
## Q(df = 6) = 192.8175, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0767 0.0126 -6.1043 <.0001 -0.1013 -0.0521 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.4614 -14.9228 -6.9228 -9.3776 33.0772
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0349
## I^2 (residual heterogeneity / unaccounted variability): 96.90%
## H^2 (unaccounted variability / sampling variability): 32.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 104.0509, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.4584, p-val = 0.4823
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0843 0.0252 -3.3417 0.0008 -0.1337 -0.0349
## continentEurope -0.0055 0.0329 -0.1680 0.8666 -0.0701 0.0590
## continentNorth America 0.0324 0.0355 0.9119 0.3618 -0.0372 0.1020
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.3990 -18.7981 -12.7981 -13.9698 11.2019
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0340
## I^2 (residual heterogeneity / unaccounted variability): 98.01%
## H^2 (unaccounted variability / sampling variability): 50.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 176.8991, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5606, p-val = 0.4540
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1451 0.0921 -1.5763 0.1150 -0.3255 0.0353
## mean.age 0.0013 0.0018 0.7487 0.4540 -0.0022 0.0048
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.5819 -23.1638 -19.1638 -19.5803 -15.1638
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0321
## I^2 (total heterogeneity / total variability): 97.75%
## H^2 (total variability / sampling variability): 44.45
##
## Test for Heterogeneity:
## Q(df = 6) = 192.8175, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0767 0.0126 -6.1043 <.0001 -0.1013 -0.0521 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.2626 -22.5253 -18.5253 -18.9418 -14.5253
##
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0342
## I^2 (total heterogeneity / total variability): 98.07%
## H^2 (total variability / sampling variability): 51.88
##
## Test for Heterogeneity:
## Q(df = 6) = 205.7391, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0775 0.0134 -5.8080 <.0001 -0.1037 -0.0514 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6895 -9.3790 -5.3790 -5.7955 -1.3790
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0072)
## tau (square root of estimated tau^2 value): 0.1086
## I^2 (total heterogeneity / total variability): 98.21%
## H^2 (total variability / sampling variability): 55.78
##
## Test for Heterogeneity:
## Q(df = 6) = 284.3511, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2392 0.0424 -5.6470 <.0001 -0.3222 -0.1562 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.2889 -14.5777 -6.5777 -9.0325 33.4223
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0367
## I^2 (residual heterogeneity / unaccounted variability): 97.23%
## H^2 (unaccounted variability / sampling variability): 36.10
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 107.9715, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.5427, p-val = 0.4624
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0868 0.0265 -3.2810 0.0010 -0.1387 -0.0350 **
## continentEurope -0.0040 0.0345 -0.1168 0.9070 -0.0717 0.0636
## continentNorth America 0.0363 0.0373 0.9738 0.3302 -0.0368 0.1094
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.1904 -6.3808 1.6192 -0.8356 41.6192
##
## tau^2 (estimated amount of residual heterogeneity): 0.0114 (SE = 0.0087)
## tau (square root of estimated tau^2 value): 0.1067
## I^2 (residual heterogeneity / unaccounted variability): 96.98%
## H^2 (unaccounted variability / sampling variability): 33.08
## R^2 (amount of heterogeneity accounted for): 3.46%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 178.2441, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.1733, p-val = 0.3373
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3311 0.0768 -4.3112 <.0001 -0.4816 -0.1806
## continentEurope 0.1143 0.1008 1.1338 0.2569 -0.0833 0.3118
## continentNorth America 0.1521 0.1082 1.4057 0.1598 -0.0600 0.3642
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.0671 -18.1342 -12.1342 -13.3059 11.8658
##
## tau^2 (estimated amount of residual heterogeneity): 0.0014 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0368
## I^2 (residual heterogeneity / unaccounted variability): 98.35%
## H^2 (unaccounted variability / sampling variability): 60.47
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 195.2758, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3968, p-val = 0.5288
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1399 0.0996 -1.4050 0.1600 -0.3350 0.0553
## mean.age 0.0012 0.0019 0.6299 0.5288 -0.0026 0.0050
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6041 -7.2083 -1.2083 -2.3800 22.7917
##
## tau^2 (estimated amount of residual heterogeneity): 0.0134 (SE = 0.0090)
## tau (square root of estimated tau^2 value): 0.1156
## I^2 (residual heterogeneity / unaccounted variability): 98.26%
## H^2 (unaccounted variability / sampling variability): 57.38
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 233.8725, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3336, p-val = 0.5635
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4172 0.3113 -1.3400 0.1802 -1.0274 0.1930
## mean.age 0.0035 0.0060 0.5776 0.5635 -0.0083 0.0153
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.2626 -22.5253 -18.5253 -18.9418 -14.5253
##
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0342
## I^2 (total heterogeneity / total variability): 98.07%
## H^2 (total variability / sampling variability): 51.88
##
## Test for Heterogeneity:
## Q(df = 6) = 205.7391, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0775 0.0134 -5.8080 <.0001 -0.1037 -0.0514 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6895 -9.3790 -5.3790 -5.7955 -1.3790
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0072)
## tau (square root of estimated tau^2 value): 0.1086
## I^2 (total heterogeneity / total variability): 98.21%
## H^2 (total variability / sampling variability): 55.78
##
## Test for Heterogeneity:
## Q(df = 6) = 284.3511, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2392 0.0424 -5.6470 <.0001 -0.3222 -0.1562 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.4637 -20.9274 -16.9274 -17.3438 -12.9274
##
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0363
## I^2 (total heterogeneity / total variability): 96.41%
## H^2 (total variability / sampling variability): 27.87
##
## Test for Heterogeneity:
## Q(df = 6) = 92.7450, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0806 0.0145 -5.5610 <.0001 -0.1091 -0.0522 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6721 -9.3442 -5.3442 -5.7607 -1.3442
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0073)
## tau (square root of estimated tau^2 value): 0.1087
## I^2 (total heterogeneity / total variability): 97.61%
## H^2 (total variability / sampling variability): 41.77
##
## Test for Heterogeneity:
## Q(df = 6) = 257.8682, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2311 0.0425 -5.4373 <.0001 -0.3144 -0.1478 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.9104 -27.8209 -23.8209 -24.2373 -19.8209
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0190
## I^2 (total heterogeneity / total variability): 79.78%
## H^2 (total variability / sampling variability): 4.94
##
## Test for Heterogeneity:
## Q(df = 6) = 32.0022, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0074 0.0088 0.8469 0.3971 -0.0098 0.0246
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.5136 -13.0272 -5.0272 -7.4820 34.9728
##
## tau^2 (estimated amount of residual heterogeneity): 0.0018 (SE = 0.0014)
## tau (square root of estimated tau^2 value): 0.0422
## I^2 (residual heterogeneity / unaccounted variability): 95.69%
## H^2 (unaccounted variability / sampling variability): 23.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 64.1273, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.0897, p-val = 0.5799
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0940 0.0308 -3.0520 0.0023 -0.1544 -0.0336 **
## continentEurope 0.0020 0.0404 0.0496 0.9604 -0.0771 0.0811
## continentNorth America 0.0391 0.0433 0.9028 0.3666 -0.0458 0.1241
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0362 -6.0724 1.9276 -0.5272 41.9276
##
## tau^2 (estimated amount of residual heterogeneity): 0.0123 (SE = 0.0094)
## tau (square root of estimated tau^2 value): 0.1109
## I^2 (residual heterogeneity / unaccounted variability): 96.47%
## H^2 (unaccounted variability / sampling variability): 28.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 174.4541, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.7403, p-val = 0.4189
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3140 0.0798 -3.9350 <.0001 -0.4704 -0.1576
## continentEurope 0.0975 0.1046 0.9318 0.3515 -0.1075 0.3025
## continentNorth America 0.1455 0.1129 1.2890 0.1974 -0.0757 0.3667
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0194
## I^2 (residual heterogeneity / unaccounted variability): 71.00%
## H^2 (unaccounted variability / sampling variability): 3.45
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 11.7950, p-val = 0.0189
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.9695, p-val = 0.6158
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0188 0.0166 1.1344 0.2566 -0.0137 0.0513
## continentEurope -0.0212 0.0218 -0.9736 0.3302 -0.0640 0.0215
## continentNorth America -0.0095 0.0230 -0.4111 0.6810 -0.0546 0.0357
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.7135 -17.4271 -11.4271 -12.5987 12.5729
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0358
## I^2 (residual heterogeneity / unaccounted variability): 96.36%
## H^2 (unaccounted variability / sampling variability): 27.49
## R^2 (amount of heterogeneity accounted for): 3.07%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 69.9653, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2655, p-val = 0.2606
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1900 0.0984 -1.9317 0.0534 -0.3829 0.0028 .
## mean.age 0.0021 0.0019 1.1249 0.2606 -0.0016 0.0059
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5750 -7.1500 -1.1500 -2.3217 22.8500
##
## tau^2 (estimated amount of residual heterogeneity): 0.0135 (SE = 0.0091)
## tau (square root of estimated tau^2 value): 0.1161
## I^2 (residual heterogeneity / unaccounted variability): 97.94%
## H^2 (unaccounted variability / sampling variability): 48.60
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 243.5127, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2861, p-val = 0.5928
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3982 0.3154 -1.2624 0.2068 -1.0164 0.2200
## mean.age 0.0033 0.0061 0.5348 0.5928 -0.0087 0.0152
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0144
## I^2 (residual heterogeneity / unaccounted variability): 69.66%
## H^2 (unaccounted variability / sampling variability): 3.30
## R^2 (amount of heterogeneity accounted for): 42.24%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 18.0249, p-val = 0.0029
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.5823, p-val = 0.0584
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0971 0.0479 2.0287 0.0425 0.0033 0.1909 *
## mean.age -0.0018 0.0009 -1.8927 0.0584 -0.0036 0.0001 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.4637 -20.9274 -16.9274 -17.3438 -12.9274
##
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0363
## I^2 (total heterogeneity / total variability): 96.41%
## H^2 (total variability / sampling variability): 27.87
##
## Test for Heterogeneity:
## Q(df = 6) = 92.7450, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0806 0.0145 -5.5610 <.0001 -0.1091 -0.0522 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6721 -9.3442 -5.3442 -5.7607 -1.3442
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0073)
## tau (square root of estimated tau^2 value): 0.1087
## I^2 (total heterogeneity / total variability): 97.61%
## H^2 (total variability / sampling variability): 41.77
##
## Test for Heterogeneity:
## Q(df = 6) = 257.8682, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2311 0.0425 -5.4373 <.0001 -0.3144 -0.1478 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.9104 -27.8209 -23.8209 -24.2373 -19.8209
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0190
## I^2 (total heterogeneity / total variability): 79.78%
## H^2 (total variability / sampling variability): 4.94
##
## Test for Heterogeneity:
## Q(df = 6) = 32.0022, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0074 0.0088 0.8469 0.3971 -0.0098 0.0246
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.2079 -14.4159 -10.4159 -12.2186 1.5841
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0213
## I^2 (total heterogeneity / total variability): 96.04%
## H^2 (total variability / sampling variability): 25.25
##
## Test for Heterogeneity:
## Q(df = 3) = 63.5945, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0838 0.0110 -7.5977 <.0001 -0.1054 -0.0622 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.6378 -17.2756 -13.2756 -15.0784 -1.2756
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0133
## I^2 (total heterogeneity / total variability): 97.33%
## H^2 (total variability / sampling variability): 37.51
##
## Test for Heterogeneity:
## Q(df = 3) = 174.4010, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0017 0.0069 -0.2438 0.8074 -0.0152 0.0118
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.8784 -7.7569 0.2431 -7.7569 40.2431
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.1527, p-val = 0.6959
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 63.4417, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0949 0.0039 -24.5103 <.0001 -0.1025 -0.0873
## continentEurope -0.0019 0.0044 -0.4375 0.6617 -0.0104 0.0066
## continentNorth America 0.0434 0.0066 6.5634 <.0001 0.0305 0.0564
##
## intrcpt ***
## continentEurope
## continentNorth America ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.5526 -5.1053 2.8947 -5.1053 42.8947
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0182
## I^2 (residual heterogeneity / unaccounted variability): 93.04%
## H^2 (unaccounted variability / sampling variability): 14.38
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.3760, p-val = 0.0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.5824, p-val = 0.7474
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0076 0.0133 -0.5729 0.5667 -0.0337 0.0185
## continentEurope 0.0172 0.0225 0.7630 0.4454 -0.0270 0.0614
## continentNorth America 0.0063 0.0227 0.2778 0.7812 -0.0382 0.0508
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.4296 -8.8591 -2.8591 -6.7797 21.1409
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0259
## I^2 (residual heterogeneity / unaccounted variability): 96.44%
## H^2 (unaccounted variability / sampling variability): 28.06
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 63.5800, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0411, p-val = 0.8393
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1155 0.1571 -0.7353 0.4621 -0.4235 0.1924
## mean.age 0.0007 0.0033 0.2028 0.8393 -0.0058 0.0071
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.3794 -14.7588 -8.7588 -12.6793 15.2412
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0000)
## tau (square root of estimated tau^2 value): 0.0052
## I^2 (residual heterogeneity / unaccounted variability): 78.35%
## H^2 (unaccounted variability / sampling variability): 4.62
## R^2 (amount of heterogeneity accounted for): 84.57%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 9.8418, p-val = 0.0073
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 14.3505, p-val = 0.0002
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1404 0.0366 -3.8364 0.0001 -0.2121 -0.0687 ***
## mean.age 0.0030 0.0008 3.7882 0.0002 0.0014 0.0045 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.2079 -14.4159 -10.4159 -12.2186 1.5841
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0213
## I^2 (total heterogeneity / total variability): 96.04%
## H^2 (total variability / sampling variability): 25.25
##
## Test for Heterogeneity:
## Q(df = 3) = 63.5945, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0838 0.0110 -7.5977 <.0001 -0.1054 -0.0622 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.6378 -17.2756 -13.2756 -15.0784 -1.2756
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0133
## I^2 (total heterogeneity / total variability): 97.33%
## H^2 (total variability / sampling variability): 37.51
##
## Test for Heterogeneity:
## Q(df = 3) = 174.4010, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0017 0.0069 -0.2438 0.8074 -0.0152 0.0118
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.9518 -13.9036 -9.9036 -11.7064 2.0964
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0231
## I^2 (total heterogeneity / total variability): 96.71%
## H^2 (total variability / sampling variability): 30.38
##
## Test for Heterogeneity:
## Q(df = 3) = 69.7329, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0842 0.0119 -7.0709 <.0001 -0.1075 -0.0608 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.8141 -17.6282 -13.6282 -15.4310 -1.6282
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0125
## I^2 (total heterogeneity / total variability): 97.09%
## H^2 (total variability / sampling variability): 34.37
##
## Test for Heterogeneity:
## Q(df = 3) = 163.4501, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0019 0.0065 -0.2928 0.7697 -0.0147 0.0109
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9033 -5.8067 -1.8067 -3.6095 10.1933
##
## tau^2 (estimated amount of total heterogeneity): 0.0081 (SE = 0.0069)
## tau (square root of estimated tau^2 value): 0.0900
## I^2 (total heterogeneity / total variability): 97.85%
## H^2 (total variability / sampling variability): 46.60
##
## Test for Heterogeneity:
## Q(df = 3) = 94.2788, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2864 0.0458 -6.2497 <.0001 -0.3762 -0.1966 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5250 -7.0500 0.9500 -7.0500 40.9500
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.8964, p-val = 0.3437
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 68.8365, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0926 0.0038 -24.3258 <.0001 -0.1000 -0.0851
## continentEurope -0.0039 0.0043 -0.9013 0.3674 -0.0122 0.0045
## continentNorth America 0.0434 0.0066 6.6023 <.0001 0.0305 0.0563
##
## intrcpt ***
## continentEurope
## continentNorth America ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6318 -5.2637 2.7363 -5.2637 42.7363
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0167
## I^2 (residual heterogeneity / unaccounted variability): 92.13%
## H^2 (unaccounted variability / sampling variability): 12.70
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.7009, p-val = 0.0004
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.6519, p-val = 0.7218
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0078 0.0123 -0.6330 0.5267 -0.0319 0.0163
## continentEurope 0.0168 0.0208 0.8071 0.4196 -0.0239 0.0574
## continentNorth America 0.0063 0.0209 0.3018 0.7628 -0.0347 0.0473
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1327 -4.2654 3.7346 -4.2654 43.7346
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0202
## I^2 (residual heterogeneity / unaccounted variability): 49.47%
## H^2 (unaccounted variability / sampling variability): 1.98
## R^2 (amount of heterogeneity accounted for): 94.98%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.9790, p-val = 0.1595
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 34.2919, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3235 0.0191 -16.9022 <.0001 -0.3610 -0.2860
## continentEurope -0.0117 0.0287 -0.4071 0.6839 -0.0679 0.0446
## continentNorth America 0.1730 0.0331 5.2319 <.0001 0.1082 0.2378
##
## intrcpt ***
## continentEurope
## continentNorth America ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.2380 -8.4760 -2.4760 -6.3965 21.5240
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0285
## I^2 (residual heterogeneity / unaccounted variability): 97.10%
## H^2 (unaccounted variability / sampling variability): 34.46
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 68.6838, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0132, p-val = 0.9084
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0645 0.1722 -0.3747 0.7079 -0.4020 0.2730
## mean.age -0.0004 0.0036 -0.1150 0.9084 -0.0075 0.0067
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.4026 -14.8053 -8.8053 -12.7259 15.1947
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0000)
## tau (square root of estimated tau^2 value): 0.0051
## I^2 (residual heterogeneity / unaccounted variability): 77.91%
## H^2 (unaccounted variability / sampling variability): 4.53
## R^2 (amount of heterogeneity accounted for): 83.30%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 9.5913, p-val = 0.0083
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 13.1657, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1321 0.0359 -3.6819 0.0002 -0.2025 -0.0618 ***
## mean.age 0.0028 0.0008 3.6285 0.0003 0.0013 0.0043 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5391 -3.0783 2.9217 -0.9988 26.9217
##
## tau^2 (estimated amount of residual heterogeneity): 0.0122 (SE = 0.0125)
## tau (square root of estimated tau^2 value): 0.1106
## I^2 (residual heterogeneity / unaccounted variability): 98.08%
## H^2 (unaccounted variability / sampling variability): 52.12
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 92.6361, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0213, p-val = 0.8839
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1902 0.6615 -0.2875 0.7737 -1.4867 1.1063
## mean.age -0.0020 0.0139 -0.1460 0.8839 -0.0293 0.0252
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.9518 -13.9036 -9.9036 -11.7064 2.0964
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0231
## I^2 (total heterogeneity / total variability): 96.71%
## H^2 (total variability / sampling variability): 30.38
##
## Test for Heterogeneity:
## Q(df = 3) = 69.7329, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0842 0.0119 -7.0709 <.0001 -0.1075 -0.0608 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.8141 -17.6282 -13.6282 -15.4310 -1.6282
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0125
## I^2 (total heterogeneity / total variability): 97.09%
## H^2 (total variability / sampling variability): 34.37
##
## Test for Heterogeneity:
## Q(df = 3) = 163.4501, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0019 0.0065 -0.2928 0.7697 -0.0147 0.0109
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9033 -5.8067 -1.8067 -3.6095 10.1933
##
## tau^2 (estimated amount of total heterogeneity): 0.0081 (SE = 0.0069)
## tau (square root of estimated tau^2 value): 0.0900
## I^2 (total heterogeneity / total variability): 97.85%
## H^2 (total variability / sampling variability): 46.60
##
## Test for Heterogeneity:
## Q(df = 3) = 94.2788, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2864 0.0458 -6.2497 <.0001 -0.3762 -0.1966 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.5193 -5.0386 -1.0386 -3.6523 10.9614
##
## tau^2 (estimated amount of total heterogeneity): 0.0047 (SE = 0.0047)
## tau (square root of estimated tau^2 value): 0.0683
## I^2 (total heterogeneity / total variability): 98.86%
## H^2 (total variability / sampling variability): 87.97
##
## Test for Heterogeneity:
## Q(df = 2) = 230.9570, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4651 0.0397 11.7072 <.0001 0.3872 0.5430 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 0.9190 -1.8381 4.1619 -1.8381 28.1619
##
## tau^2 (estimated amount of residual heterogeneity): 0.0093 (SE = 0.0132)
## tau (square root of estimated tau^2 value): 0.0963
## I^2 (residual heterogeneity / unaccounted variability): 99.55%
## H^2 (unaccounted variability / sampling variability): 222.84
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 222.8377, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0072, p-val = 0.9326
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4717 0.0969 4.8681 <.0001 0.2818 0.6616 ***
## continentEurope -0.0100 0.1185 -0.0846 0.9326 -0.2423 0.2223
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 0.9234 -1.8468 4.1532 -1.8468 28.1532
##
## tau^2 (estimated amount of residual heterogeneity): 0.0092 (SE = 0.0131)
## tau (square root of estimated tau^2 value): 0.0958
## I^2 (residual heterogeneity / unaccounted variability): 99.46%
## H^2 (unaccounted variability / sampling variability): 184.13
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 184.1325, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0169, p-val = 0.8966
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5177 0.4093 1.2650 0.2059 -0.2844 1.3199
## mean.age -0.0009 0.0072 -0.1300 0.8966 -0.0151 0.0132
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7744 -3.5487 2.4513 -3.5487 26.4513
##
## tau^2 (estimated amount of residual heterogeneity): 0.0016 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0402
## I^2 (residual heterogeneity / unaccounted variability): 96.14%
## H^2 (unaccounted variability / sampling variability): 25.91
## R^2 (amount of heterogeneity accounted for): 65.33%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 25.9141, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.6424, p-val = 0.0312
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2037 0.1238 1.6447 0.1000 -0.0390 0.4463
## scale1 0.0271 0.0126 2.1546 0.0312 0.0024 0.0517 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6356 -9.2713 -5.2713 -7.8850 6.7287
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0229
## I^2 (total heterogeneity / total variability): 90.47%
## H^2 (total variability / sampling variability): 10.50
##
## Test for Heterogeneity:
## Q(df = 2) = 28.1947, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1000 0.0141 -7.0814 <.0001 -0.1277 -0.0723 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1103 -4.2205 1.7795 -4.2205 25.7795
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0287
## I^2 (residual heterogeneity / unaccounted variability): 95.70%
## H^2 (unaccounted variability / sampling variability): 23.24
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 23.2427, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2290, p-val = 0.6323
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0869 0.0315 -2.7587 0.0058 -0.1487 -0.0252 **
## continentEurope -0.0180 0.0377 -0.4785 0.6323 -0.0920 0.0559
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2447 -4.4894 1.5106 -4.4894 25.5106
##
## tau^2 (estimated amount of residual heterogeneity): 0.0006 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0247
## I^2 (residual heterogeneity / unaccounted variability): 92.96%
## H^2 (unaccounted variability / sampling variability): 14.20
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.2012, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6280, p-val = 0.4281
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1887 0.1132 -1.6673 0.0954 -0.4105 0.0331 .
## mean.age 0.0016 0.0020 0.7925 0.4281 -0.0024 0.0055
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1912 -4.3825 1.6175 -4.3825 25.6175
##
## tau^2 (estimated amount of residual heterogeneity): 0.0006 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0253
## I^2 (residual heterogeneity / unaccounted variability): 87.39%
## H^2 (unaccounted variability / sampling variability): 7.93
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.9286, p-val = 0.0049
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5605, p-val = 0.4540
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0410 0.0799 -0.5134 0.6077 -0.1977 0.1156
## scale1 -0.0061 0.0081 -0.7487 0.4540 -0.0220 0.0099
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.4849 -8.9698 -4.9698 -7.5835 7.0302
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0249
## I^2 (total heterogeneity / total variability): 91.76%
## H^2 (total variability / sampling variability): 12.13
##
## Test for Heterogeneity:
## Q(df = 2) = 33.4609, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1003 0.0152 -6.5945 <.0001 -0.1300 -0.0705 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.0044 -4.0088 1.9912 -4.0088 25.9912
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0320
## I^2 (residual heterogeneity / unaccounted variability): 96.45%
## H^2 (unaccounted variability / sampling variability): 28.18
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 28.1843, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1685, p-val = 0.6814
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0880 0.0345 -2.5500 0.0108 -0.1557 -0.0204 *
## continentEurope -0.0170 0.0415 -0.4105 0.6814 -0.0984 0.0643
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1213 -4.2425 1.7575 -4.2425 25.7575
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0282
## I^2 (residual heterogeneity / unaccounted variability): 94.41%
## H^2 (unaccounted variability / sampling variability): 17.88
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 17.8824, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4918, p-val = 0.4831
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1882 0.1269 -1.4831 0.1380 -0.4368 0.0605
## mean.age 0.0016 0.0023 0.7013 0.4831 -0.0028 0.0060
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1568 -4.3136 1.6864 -4.3136 25.6864
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0263
## I^2 (residual heterogeneity / unaccounted variability): 88.47%
## H^2 (unaccounted variability / sampling variability): 8.67
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.6732, p-val = 0.0032
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6974, p-val = 0.4037
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0321 0.0830 -0.3867 0.6990 -0.1948 0.1306
## scale1 -0.0071 0.0084 -0.8351 0.4037 -0.0236 0.0095
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.4676 -8.9352 -4.9352 -7.5489 7.0648
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0251
## I^2 (total heterogeneity / total variability): 92.27%
## H^2 (total variability / sampling variability): 12.94
##
## Test for Heterogeneity:
## Q(df = 2) = 35.9401, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1074 0.0153 -7.0185 <.0001 -0.1374 -0.0774 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7846 -5.5692 -1.5692 -4.1829 10.4308
##
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0560
## I^2 (total heterogeneity / total variability): 88.84%
## H^2 (total variability / sampling variability): 8.96
##
## Test for Heterogeneity:
## Q(df = 2) = 16.3845, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3937 0.0344 -11.4325 <.0001 -0.4612 -0.3262 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9907 -3.9814 2.0186 -3.9814 26.0186
##
## tau^2 (estimated amount of residual heterogeneity): 0.0011 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0325
## I^2 (residual heterogeneity / unaccounted variability): 96.69%
## H^2 (unaccounted variability / sampling variability): 30.22
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 30.2239, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1623, p-val = 0.6871
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0953 0.0348 -2.7384 0.0062 -0.1636 -0.0271 **
## continentEurope -0.0169 0.0419 -0.4028 0.6871 -0.0991 0.0653
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1383 -2.2766 3.7234 -2.2766 27.7234
##
## tau^2 (estimated amount of residual heterogeneity): 0.0056 (SE = 0.0085)
## tau (square root of estimated tau^2 value): 0.0751
## I^2 (residual heterogeneity / unaccounted variability): 93.79%
## H^2 (unaccounted variability / sampling variability): 16.11
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 16.1108, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1979, p-val = 0.6564
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4218 0.0787 -5.3571 <.0001 -0.5761 -0.2675 ***
## continentEurope 0.0427 0.0959 0.4449 0.6564 -0.1453 0.2307
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1055 -4.2109 1.7891 -4.2109 25.7891
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0287
## I^2 (residual heterogeneity / unaccounted variability): 94.81%
## H^2 (unaccounted variability / sampling variability): 19.28
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 19.2772, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4767, p-val = 0.4899
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1951 0.1285 -1.5180 0.1290 -0.4470 0.0568
## mean.age 0.0016 0.0023 0.6905 0.4899 -0.0029 0.0060
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.0644 -2.1288 3.8712 -2.1288 27.8712
##
## tau^2 (estimated amount of residual heterogeneity): 0.0065 (SE = 0.0099)
## tau (square root of estimated tau^2 value): 0.0809
## I^2 (residual heterogeneity / unaccounted variability): 93.85%
## H^2 (unaccounted variability / sampling variability): 16.25
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 16.2482, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0441, p-val = 0.8337
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3192 0.3545 -0.9003 0.3679 -1.0139 0.3756
## mean.age -0.0013 0.0062 -0.2100 0.8337 -0.0136 0.0109
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1519 -4.3038 1.6962 -4.3038 25.6962
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0266
## I^2 (residual heterogeneity / unaccounted variability): 89.30%
## H^2 (unaccounted variability / sampling variability): 9.35
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.3483, p-val = 0.0022
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7082, p-val = 0.4001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0383 0.0836 -0.4576 0.6472 -0.2021 0.1256
## scale1 -0.0072 0.0085 -0.8415 0.4001 -0.0238 0.0095
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9697 -5.9394 0.0606 -5.9394 24.0606
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.1442, p-val = 0.7041
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 16.2403, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1346 0.0692 -1.9432 0.0520 -0.2703 0.0012 .
## scale1 -0.0268 0.0067 -4.0299 <.0001 -0.0399 -0.0138 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.0033 -10.0066 -6.0066 -8.6203 5.9934
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0160
## I^2 (total heterogeneity / total variability): 70.46%
## H^2 (total variability / sampling variability): 3.39
##
## Test for Heterogeneity:
## Q(df = 2) = 7.1299, p-val = 0.0283
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1250 0.0113 -11.0163 <.0001 -0.1472 -0.1027 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8701 -5.7402 -1.7402 -4.3539 10.2598
##
## tau^2 (estimated amount of total heterogeneity): 0.0024 (SE = 0.0034)
## tau (square root of estimated tau^2 value): 0.0491
## I^2 (total heterogeneity / total variability): 78.69%
## H^2 (total variability / sampling variability): 4.69
##
## Test for Heterogeneity:
## Q(df = 2) = 10.3844, p-val = 0.0056
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.4037 0.0334 -12.0808 <.0001 -0.4691 -0.3382 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.2941 -8.5881 -4.5881 -7.2018 7.4119
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0239
## I^2 (total heterogeneity / total variability): 72.71%
## H^2 (total variability / sampling variability): 3.66
##
## Test for Heterogeneity:
## Q(df = 2) = 7.9259, p-val = 0.0190
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0284 0.0165 1.7193 0.0856 -0.0040 0.0608 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6212 -5.2424 0.7576 -5.2424 24.7576
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0155
## I^2 (residual heterogeneity / unaccounted variability): 77.83%
## H^2 (unaccounted variability / sampling variability): 4.51
## R^2 (amount of heterogeneity accounted for): 6.21%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.5101, p-val = 0.0337
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9489, p-val = 0.3300
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1030 0.0253 -4.0791 <.0001 -0.1525 -0.0535 ***
## continentEurope -0.0274 0.0281 -0.9741 0.3300 -0.0825 0.0277
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3515 -2.7030 3.2970 -2.7030 27.2970
##
## tau^2 (estimated amount of residual heterogeneity): 0.0035 (SE = 0.0055)
## tau (square root of estimated tau^2 value): 0.0594
## I^2 (residual heterogeneity / unaccounted variability): 90.07%
## H^2 (unaccounted variability / sampling variability): 10.07
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 10.0712, p-val = 0.0015
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3538, p-val = 0.5520
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4474 0.0823 -5.4365 <.0001 -0.6087 -0.2861 ***
## continentEurope 0.0556 0.0934 0.5948 0.5520 -0.1275 0.2387
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0314
## I^2 (residual heterogeneity / unaccounted variability): 87.21%
## H^2 (unaccounted variability / sampling variability): 7.82
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.8207, p-val = 0.0052
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2075, p-val = 0.6487
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0127 0.0404 0.3130 0.7543 -0.0666 0.0919
## continentEurope 0.0213 0.0469 0.4555 0.6487 -0.0705 0.1132
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9434 -5.8867 0.1133 -5.8867 24.1133
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0086
## I^2 (residual heterogeneity / unaccounted variability): 45.74%
## H^2 (unaccounted variability / sampling variability): 1.84
## R^2 (amount of heterogeneity accounted for): 71.06%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.8428, p-val = 0.1746
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.7629, p-val = 0.0965
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2445 0.0701 -3.4902 0.0005 -0.3818 -0.1072 ***
## mean.age 0.0022 0.0013 1.6622 0.0965 -0.0004 0.0048 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2226 -2.4451 3.5549 -2.4451 27.5549
##
## tau^2 (estimated amount of residual heterogeneity): 0.0045 (SE = 0.0072)
## tau (square root of estimated tau^2 value): 0.0674
## I^2 (residual heterogeneity / unaccounted variability): 89.59%
## H^2 (unaccounted variability / sampling variability): 9.61
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.6083, p-val = 0.0019
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1012, p-val = 0.7504
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2985 0.3369 -0.8860 0.3756 -0.9589 0.3619
## mean.age -0.0019 0.0061 -0.3182 0.7504 -0.0139 0.0100
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0019)
## tau (square root of estimated tau^2 value): 0.0343
## I^2 (residual heterogeneity / unaccounted variability): 86.69%
## H^2 (unaccounted variability / sampling variability): 7.51
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.5112, p-val = 0.0061
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0422, p-val = 0.8372
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0626 0.1671 0.3747 0.7079 -0.2649 0.3901
## mean.age -0.0006 0.0030 -0.2055 0.8372 -0.0065 0.0053
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1930 -4.3861 1.6139 -4.3861 25.6139
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0227
## I^2 (residual heterogeneity / unaccounted variability): 70.85%
## H^2 (unaccounted variability / sampling variability): 3.43
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 3.4308, p-val = 0.0640
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1168, p-val = 0.7325
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0977 0.0760 -1.2858 0.1985 -0.2467 0.0512
## scale1 -0.0027 0.0078 -0.3418 0.7325 -0.0179 0.0126
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2410 -4.4819 1.5181 -4.4819 25.5181
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0561, p-val = 0.8127
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 10.3282, p-val = 0.0013
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1888 0.0716 -2.6370 0.0084 -0.3292 -0.0485 **
## scale1 -0.0223 0.0069 -3.2138 0.0013 -0.0359 -0.0087 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0544 -6.1088 -0.1088 -6.1088 23.8912
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0004, p-val = 0.9842
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.9255, p-val = 0.0049
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1427 0.0438 3.2562 0.0011 0.0568 0.2286 **
## scale1 -0.0119 0.0042 -2.8152 0.0049 -0.0201 -0.0036 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 14.3821 -28.7643 -24.7643 -22.8754 -24.0143
##
## tau^2 (estimated amount of total heterogeneity): 0.0122 (SE = 0.0042)
## tau (square root of estimated tau^2 value): 0.1105
## I^2 (total heterogeneity / total variability): 99.39%
## H^2 (total variability / sampling variability): 165.12
##
## Test for Heterogeneity:
## Q(df = 19) = 2183.3189, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3620 0.0253 14.3022 <.0001 0.3124 0.4116 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.9834 -23.9668 -13.9668 -10.1039 -7.9668
##
## tau^2 (estimated amount of residual heterogeneity): 0.0125 (SE = 0.0046)
## tau (square root of estimated tau^2 value): 0.1116
## I^2 (residual heterogeneity / unaccounted variability): 98.96%
## H^2 (unaccounted variability / sampling variability): 95.73
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 2113.1540, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.6844, p-val = 0.4429
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2601 0.1207 2.1562 0.0311 0.0237 0.4966 *
## continentEurope 0.0939 0.1238 0.7584 0.4482 -0.1487 0.3365
## continentNorth America 0.2033 0.1647 1.2340 0.2172 -0.1196 0.5261
## continentOceania 0.2174 0.1644 1.3225 0.1860 -0.1048 0.5395
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 17.5947 -35.1894 -29.1894 -26.5182 -27.4751
##
## tau^2 (estimated amount of residual heterogeneity): 0.0077 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0.0879
## I^2 (residual heterogeneity / unaccounted variability): 99.00%
## H^2 (unaccounted variability / sampling variability): 100.32
## R^2 (amount of heterogeneity accounted for): 36.79%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 1793.1801, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 11.4540, p-val = 0.0007
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.0059 0.1909 5.2705 <.0001 0.6318 1.3800 ***
## mean.age -0.0104 0.0031 -3.3844 0.0007 -0.0165 -0.0044 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 14.7012 -29.4025 -23.4025 -20.7314 -21.6882
##
## tau^2 (estimated amount of residual heterogeneity): 0.0108 (SE = 0.0038)
## tau (square root of estimated tau^2 value): 0.1038
## I^2 (residual heterogeneity / unaccounted variability): 99.06%
## H^2 (unaccounted variability / sampling variability): 106.46
## R^2 (amount of heterogeneity accounted for): 11.73%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 2154.3410, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.4514, p-val = 0.0632
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2622 0.0590 4.4439 <.0001 0.1465 0.3778 ***
## scale2 0.0199 0.0107 1.8578 0.0632 -0.0011 0.0408 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.9609 -61.9217 -57.9217 -56.0328 -57.1717
##
## tau^2 (estimated amount of total heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0436
## I^2 (total heterogeneity / total variability): 97.03%
## H^2 (total variability / sampling variability): 33.68
##
## Test for Heterogeneity:
## Q(df = 19) = 398.5644, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1128 0.0104 -10.8620 <.0001 -0.1332 -0.0925 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 25.6893 -51.3787 -41.3787 -37.5157 -35.3787
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0446
## I^2 (residual heterogeneity / unaccounted variability): 95.69%
## H^2 (unaccounted variability / sampling variability): 23.22
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 176.0198, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.0780, p-val = 0.5564
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1377 0.0559 -2.4640 0.0137 -0.2473 -0.0282 *
## continentEurope 0.0221 0.0571 0.3869 0.6989 -0.0898 0.1339
## continentNorth America 0.0218 0.0728 0.2999 0.7643 -0.1209 0.1646
## continentOceania 0.0851 0.0716 1.1890 0.2345 -0.0552 0.2254
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.5690 -63.1380 -57.1380 -54.4669 -55.4237
##
## tau^2 (estimated amount of residual heterogeneity): 0.0014 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0369
## I^2 (residual heterogeneity / unaccounted variability): 95.52%
## H^2 (unaccounted variability / sampling variability): 22.32
## R^2 (amount of heterogeneity accounted for): 28.70%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 201.3723, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.7292, p-val = 0.0095
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0981 0.0815 1.2031 0.2289 -0.0617 0.2578
## mean.age -0.0034 0.0013 -2.5941 0.0095 -0.0060 -0.0008 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 29.2781 -58.5563 -52.5563 -49.8852 -50.8420
##
## tau^2 (estimated amount of residual heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0438
## I^2 (residual heterogeneity / unaccounted variability): 95.98%
## H^2 (unaccounted variability / sampling variability): 24.88
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 362.4088, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9895, p-val = 0.3199
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1362 0.0257 -5.3019 <.0001 -0.1866 -0.0859 ***
## scale2 0.0046 0.0046 0.9948 0.3199 -0.0045 0.0136
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.6562 -61.3124 -57.3124 -55.4235 -56.5624
##
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0447
## I^2 (total heterogeneity / total variability): 96.85%
## H^2 (total variability / sampling variability): 31.72
##
## Test for Heterogeneity:
## Q(df = 19) = 370.8055, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1130 0.0106 -10.6696 <.0001 -0.1337 -0.0922 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 25.3990 -50.7980 -40.7980 -36.9351 -34.7980
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0459
## I^2 (residual heterogeneity / unaccounted variability): 95.76%
## H^2 (unaccounted variability / sampling variability): 23.56
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 187.4807, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.9953, p-val = 0.5734
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1419 0.0562 -2.5262 0.0115 -0.2520 -0.0318 *
## continentEurope 0.0265 0.0574 0.4612 0.6447 -0.0860 0.1390
## continentNorth America 0.0255 0.0738 0.3450 0.7301 -0.1192 0.1701
## continentOceania 0.0885 0.0726 1.2193 0.2227 -0.0538 0.2307
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.8793 -61.7586 -55.7586 -53.0875 -54.0443
##
## tau^2 (estimated amount of residual heterogeneity): 0.0015 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0392
## I^2 (residual heterogeneity / unaccounted variability): 95.59%
## H^2 (unaccounted variability / sampling variability): 22.67
## R^2 (amount of heterogeneity accounted for): 23.20%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 212.5169, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.5323, p-val = 0.0187
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0895 0.0864 1.0362 0.3001 -0.0798 0.2588
## mean.age -0.0033 0.0014 -2.3521 0.0187 -0.0060 -0.0005 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 28.9455 -57.8910 -51.8910 -49.2199 -50.1767
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0450
## I^2 (residual heterogeneity / unaccounted variability): 95.88%
## H^2 (unaccounted variability / sampling variability): 24.24
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 349.9235, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8848, p-val = 0.3469
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1356 0.0263 -5.1594 <.0001 -0.1871 -0.0841 ***
## scale2 0.0045 0.0047 0.9407 0.3469 -0.0048 0.0137
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.3718 -62.7436 -58.7436 -56.8547 -57.9936
##
## tau^2 (estimated amount of total heterogeneity): 0.0018 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0429
## I^2 (total heterogeneity / total variability): 96.68%
## H^2 (total variability / sampling variability): 30.09
##
## Test for Heterogeneity:
## Q(df = 19) = 368.9261, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1139 0.0102 -11.1850 <.0001 -0.1339 -0.0940 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 14.5166 -29.0332 -25.0332 -23.1444 -24.2832
##
## tau^2 (estimated amount of total heterogeneity): 0.0120 (SE = 0.0042)
## tau (square root of estimated tau^2 value): 0.1093
## I^2 (total heterogeneity / total variability): 96.56%
## H^2 (total variability / sampling variability): 29.05
##
## Test for Heterogeneity:
## Q(df = 19) = 1010.6427, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2717 0.0254 -10.6780 <.0001 -0.3216 -0.2218 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 26.1499 -52.2997 -42.2997 -38.4368 -36.2997
##
## tau^2 (estimated amount of residual heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0436
## I^2 (residual heterogeneity / unaccounted variability): 95.45%
## H^2 (unaccounted variability / sampling variability): 21.98
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 170.5647, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.3348, p-val = 0.5059
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1474 0.0540 -2.7280 0.0064 -0.2533 -0.0415 **
## continentEurope 0.0315 0.0552 0.5714 0.5677 -0.0766 0.1397
## continentNorth America 0.0236 0.0707 0.3340 0.7384 -0.1149 0.1621
## continentOceania 0.0932 0.0694 1.3418 0.1797 -0.0429 0.2293
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.1153 -26.2307 -16.2307 -12.3677 -10.2307
##
## tau^2 (estimated amount of residual heterogeneity): 0.0105 (SE = 0.0040)
## tau (square root of estimated tau^2 value): 0.1024
## I^2 (residual heterogeneity / unaccounted variability): 95.06%
## H^2 (unaccounted variability / sampling variability): 20.25
## R^2 (amount of heterogeneity accounted for): 12.27%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 480.3704, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.1956, p-val = 0.1580
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2941 0.1160 -2.5358 0.0112 -0.5214 -0.0668 *
## continentEurope 0.0186 0.1188 0.1567 0.8755 -0.2143 0.2516
## continentNorth America -0.1007 0.1568 -0.6425 0.5205 -0.4080 0.2065
## continentOceania 0.2219 0.1549 1.4325 0.1520 -0.0817 0.5255
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.8571 -63.7141 -57.7141 -55.0430 -55.9999
##
## tau^2 (estimated amount of residual heterogeneity): 0.0014 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0369
## I^2 (residual heterogeneity / unaccounted variability): 95.21%
## H^2 (unaccounted variability / sampling variability): 20.88
## R^2 (amount of heterogeneity accounted for): 25.83%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 219.6926, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.3061, p-val = 0.0120
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0906 0.0817 1.1091 0.2674 -0.0695 0.2506
## mean.age -0.0033 0.0013 -2.5112 0.0120 -0.0059 -0.0007 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.4274 -26.8548 -20.8548 -18.1836 -19.1405
##
## tau^2 (estimated amount of residual heterogeneity): 0.0124 (SE = 0.0045)
## tau (square root of estimated tau^2 value): 0.1113
## I^2 (residual heterogeneity / unaccounted variability): 96.23%
## H^2 (unaccounted variability / sampling variability): 26.51
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 1003.3146, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3335, p-val = 0.5636
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4117 0.2437 -1.6896 0.0911 -0.8892 0.0659 .
## mean.age 0.0023 0.0039 0.5775 0.5636 -0.0054 0.0100
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 29.4892 -58.9783 -52.9783 -50.3072 -51.2640
##
## tau^2 (estimated amount of residual heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0436
## I^2 (residual heterogeneity / unaccounted variability): 95.72%
## H^2 (unaccounted variability / sampling variability): 23.38
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 342.9145, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6091, p-val = 0.4351
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1322 0.0255 -5.1854 <.0001 -0.1821 -0.0822 ***
## scale2 0.0036 0.0046 0.7805 0.4351 -0.0054 0.0126
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 15.7343 -31.4686 -25.4686 -22.7975 -23.7543
##
## tau^2 (estimated amount of residual heterogeneity): 0.0091 (SE = 0.0034)
## tau (square root of estimated tau^2 value): 0.0956
## I^2 (residual heterogeneity / unaccounted variability): 95.08%
## H^2 (unaccounted variability / sampling variability): 20.34
## R^2 (amount of heterogeneity accounted for): 23.59%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 473.9443, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.8991, p-val = 0.0151
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1477 0.0555 -2.6618 0.0078 -0.2565 -0.0390 **
## scale2 -0.0243 0.0100 -2.4288 0.0151 -0.0440 -0.0047 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.0899 -54.1797 -50.1797 -48.2909 -49.4297
##
## tau^2 (estimated amount of total heterogeneity): 0.0027 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0523
## I^2 (total heterogeneity / total variability): 95.50%
## H^2 (total variability / sampling variability): 22.22
##
## Test for Heterogeneity:
## Q(df = 19) = 276.9773, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1317 0.0127 -10.3514 <.0001 -0.1566 -0.1068 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.6323 -21.2646 -17.2646 -15.3757 -16.5146
##
## tau^2 (estimated amount of total heterogeneity): 0.0152 (SE = 0.0060)
## tau (square root of estimated tau^2 value): 0.1234
## I^2 (total heterogeneity / total variability): 95.06%
## H^2 (total variability / sampling variability): 20.23
##
## Test for Heterogeneity:
## Q(df = 19) = 874.0879, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3173 0.0306 -10.3622 <.0001 -0.3773 -0.2572 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 34.0688 -68.1376 -64.1376 -62.2488 -63.3876
##
## tau^2 (estimated amount of total heterogeneity): 0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0256
## I^2 (total heterogeneity / total variability): 73.04%
## H^2 (total variability / sampling variability): 3.71
##
## Test for Heterogeneity:
## Q(df = 19) = 56.0213, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0309 0.0080 3.8545 0.0001 0.0152 0.0467 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 22.8412 -45.6825 -35.6825 -31.8195 -29.6825
##
## tau^2 (estimated amount of residual heterogeneity): 0.0027 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0520
## I^2 (residual heterogeneity / unaccounted variability): 93.34%
## H^2 (unaccounted variability / sampling variability): 15.02
## R^2 (amount of heterogeneity accounted for): 1.02%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 111.7893, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.9769, p-val = 0.3952
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1796 0.0701 -2.5603 0.0105 -0.3170 -0.0421 *
## continentEurope 0.0427 0.0715 0.5974 0.5502 -0.0974 0.1828
## continentNorth America 0.0805 0.0899 0.8957 0.3704 -0.0957 0.2568
## continentOceania 0.1222 0.0874 1.3978 0.1622 -0.0491 0.2935
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.9661 -19.9323 -9.9323 -6.0693 -3.9323
##
## tau^2 (estimated amount of residual heterogeneity): 0.0123 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0.1109
## I^2 (residual heterogeneity / unaccounted variability): 90.91%
## H^2 (unaccounted variability / sampling variability): 11.01
## R^2 (amount of heterogeneity accounted for): 19.25%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 118.4667, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.4533, p-val = 0.1415
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4145 0.1792 -2.3134 0.0207 -0.7658 -0.0633 *
## continentEurope 0.0834 0.1817 0.4590 0.6462 -0.2727 0.4396
## continentNorth America 0.0997 0.2190 0.4552 0.6489 -0.3295 0.5288
## continentOceania 0.3445 0.2108 1.6339 0.1023 -0.0688 0.7578
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0207
## I^2 (residual heterogeneity / unaccounted variability): 53.86%
## H^2 (unaccounted variability / sampling variability): 2.17
## R^2 (amount of heterogeneity accounted for): 34.53%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 32.7135, p-val = 0.0081
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.4100, p-val = 0.0933
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0595 0.0674 0.8833 0.3770 -0.0725 0.1916
## continentEurope -0.0228 0.0678 -0.3365 0.7365 -0.1558 0.1101
## continentNorth America -0.1000 0.0756 -1.3235 0.1857 -0.2482 0.0481
## continentOceania -0.0530 0.0706 -0.7501 0.4532 -0.1914 0.0854
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.6317 -55.2634 -49.2634 -46.5923 -47.5491
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0451
## I^2 (residual heterogeneity / unaccounted variability): 93.59%
## H^2 (unaccounted variability / sampling variability): 15.61
## R^2 (amount of heterogeneity accounted for): 25.70%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 155.7586, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.9073, p-val = 0.0151
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1131 0.1009 1.1208 0.2624 -0.0846 0.3107
## mean.age -0.0040 0.0016 -2.4305 0.0151 -0.0072 -0.0008 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.5990 -19.1980 -13.1980 -10.5269 -11.4837
##
## tau^2 (estimated amount of residual heterogeneity): 0.0163 (SE = 0.0066)
## tau (square root of estimated tau^2 value): 0.1276
## I^2 (residual heterogeneity / unaccounted variability): 95.06%
## H^2 (unaccounted variability / sampling variability): 20.23
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 810.0881, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1349, p-val = 0.7134
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2137 0.2850 -0.7496 0.4535 -0.7723 0.3450
## mean.age -0.0017 0.0046 -0.3673 0.7134 -0.0108 0.0074
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0267
## I^2 (residual heterogeneity / unaccounted variability): 72.87%
## H^2 (unaccounted variability / sampling variability): 3.69
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 46.3798, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6195, p-val = 0.4312
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0214 0.0672 -0.3185 0.7501 -0.1530 0.1102
## mean.age 0.0009 0.0011 0.7871 0.4312 -0.0013 0.0031
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 25.8677 -51.7355 -45.7355 -43.0644 -44.0212
##
## tau^2 (estimated amount of residual heterogeneity): 0.0027 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0518
## I^2 (residual heterogeneity / unaccounted variability): 93.75%
## H^2 (unaccounted variability / sampling variability): 16.00
## R^2 (amount of heterogeneity accounted for): 1.87%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 253.3383, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.5424, p-val = 0.2143
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1669 0.0311 -5.3714 <.0001 -0.2278 -0.1060 ***
## scale2 0.0069 0.0056 1.2419 0.2143 -0.0040 0.0178
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.8119 -19.6239 -13.6239 -10.9528 -11.9096
##
## tau^2 (estimated amount of residual heterogeneity): 0.0153 (SE = 0.0062)
## tau (square root of estimated tau^2 value): 0.1239
## I^2 (residual heterogeneity / unaccounted variability): 92.97%
## H^2 (unaccounted variability / sampling variability): 14.22
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 383.0457, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5759, p-val = 0.4479
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2649 0.0756 -3.5055 0.0005 -0.4130 -0.1168 ***
## scale2 -0.0102 0.0134 -0.7589 0.4479 -0.0366 0.0161
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.2041 -64.4082 -58.4082 -55.7370 -56.6939
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0272
## I^2 (residual heterogeneity / unaccounted variability): 68.88%
## H^2 (unaccounted variability / sampling variability): 3.21
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 55.1806, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2701, p-val = 0.2597
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0519 0.0202 2.5665 0.0103 0.0123 0.0915 *
## scale2 -0.0038 0.0034 -1.1270 0.2597 -0.0105 0.0028
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.3000 -24.6001 -20.6001 -19.6303 -19.2668
##
## tau^2 (estimated amount of total heterogeneity): 0.0036 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0597
## I^2 (total heterogeneity / total variability): 96.50%
## H^2 (total variability / sampling variability): 28.59
##
## Test for Heterogeneity:
## Q(df = 12) = 75.0873, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1042 0.0208 -5.0134 <.0001 -0.1449 -0.0635 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 29.6130 -59.2261 -55.2261 -54.2562 -53.8927
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0167
## I^2 (total heterogeneity / total variability): 91.39%
## H^2 (total variability / sampling variability): 11.61
##
## Test for Heterogeneity:
## Q(df = 12) = 269.2438, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0012 0.0057 0.2132 0.8312 -0.0100 0.0125
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.7514 -21.5027 -15.5027 -14.3091 -12.0742
##
## tau^2 (estimated amount of residual heterogeneity): 0.0042 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0.0648
## I^2 (residual heterogeneity / unaccounted variability): 91.65%
## H^2 (unaccounted variability / sampling variability): 11.97
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 50.7173, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5397, p-val = 0.4625
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0605 0.0648 -0.9337 0.3505 -0.1876 0.0665
## continentEurope -0.0506 0.0689 -0.7347 0.4625 -0.1858 0.0845
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.5310 -55.0620 -49.0620 -47.8683 -45.6334
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0145
## I^2 (residual heterogeneity / unaccounted variability): 75.57%
## H^2 (unaccounted variability / sampling variability): 4.09
## R^2 (amount of heterogeneity accounted for): 24.16%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 31.1098, p-val = 0.0011
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.6416, p-val = 0.1041
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0209 0.0146 -1.4334 0.1517 -0.0495 0.0077
## continentEurope 0.0254 0.0156 1.6253 0.1041 -0.0052 0.0560
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.4073 -22.8146 -16.8146 -15.6209 -13.3860
##
## tau^2 (estimated amount of residual heterogeneity): 0.0031 (SE = 0.0021)
## tau (square root of estimated tau^2 value): 0.0556
## I^2 (residual heterogeneity / unaccounted variability): 95.14%
## H^2 (unaccounted variability / sampling variability): 20.56
## R^2 (amount of heterogeneity accounted for): 13.47%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 59.2910, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9591, p-val = 0.1616
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1336 0.1703 0.7844 0.4328 -0.2002 0.4674
## mean.age -0.0040 0.0028 -1.3997 0.1616 -0.0095 0.0016
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 26.5755 -53.1511 -47.1511 -45.9574 -43.7225
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0179
## I^2 (residual heterogeneity / unaccounted variability): 91.31%
## H^2 (unaccounted variability / sampling variability): 11.51
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 212.8375, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0201, p-val = 0.8873
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0064 0.0538 -0.1181 0.9060 -0.1118 0.0991
## mean.age 0.0001 0.0009 0.1417 0.8873 -0.0016 0.0019
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.7959 -21.5918 -15.5918 -14.3982 -12.1633
##
## tau^2 (estimated amount of residual heterogeneity): 0.0042 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0.0647
## I^2 (residual heterogeneity / unaccounted variability): 92.05%
## H^2 (unaccounted variability / sampling variability): 12.57
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 58.0105, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5703, p-val = 0.4501
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1671 0.0848 -1.9712 0.0487 -0.3333 -0.0010 *
## scale2 0.0132 0.0175 0.7552 0.4501 -0.0210 0.0474
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.5924 -55.1847 -49.1847 -47.9910 -45.7561
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0144
## I^2 (residual heterogeneity / unaccounted variability): 77.79%
## H^2 (unaccounted variability / sampling variability): 4.50
## R^2 (amount of heterogeneity accounted for): 25.38%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 39.3616, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.7049, p-val = 0.1000
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0301 0.0197 -1.5244 0.1274 -0.0687 0.0086
## scale2 0.0066 0.0040 1.6447 0.1000 -0.0013 0.0145
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.2897 -24.5794 -20.5794 -19.6096 -19.2461
##
## tau^2 (estimated amount of total heterogeneity): 0.0040 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0629
## I^2 (total heterogeneity / total variability): 96.95%
## H^2 (total variability / sampling variability): 32.83
##
## Test for Heterogeneity:
## Q(df = 12) = 108.8162, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1187 0.0215 -5.5164 <.0001 -0.1608 -0.0765 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.0344 -60.0689 -56.0689 -55.0990 -54.7355
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0162
## I^2 (total heterogeneity / total variability): 91.16%
## H^2 (total variability / sampling variability): 11.31
##
## Test for Heterogeneity:
## Q(df = 12) = 287.9791, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0045 0.0056 0.7999 0.4238 -0.0065 0.0155
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.1888 -18.3776 -14.3776 -13.4078 -13.0443
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0052)
## tau (square root of estimated tau^2 value): 0.1084
## I^2 (total heterogeneity / total variability): 96.26%
## H^2 (total variability / sampling variability): 26.77
##
## Test for Heterogeneity:
## Q(df = 12) = 627.3178, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2681 0.0312 -8.5824 <.0001 -0.3294 -0.2069 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.9416 -21.8831 -15.8831 -14.6894 -12.4545
##
## tau^2 (estimated amount of residual heterogeneity): 0.0043 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0.0654
## I^2 (residual heterogeneity / unaccounted variability): 92.12%
## H^2 (unaccounted variability / sampling variability): 12.69
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 61.0752, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8908, p-val = 0.3453
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0612 0.0654 -0.9365 0.3490 -0.1894 0.0669
## continentEurope -0.0656 0.0695 -0.9438 0.3453 -0.2018 0.0706
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 28.4516 -56.9031 -50.9031 -49.7094 -47.4746
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0124
## I^2 (residual heterogeneity / unaccounted variability): 69.92%
## H^2 (unaccounted variability / sampling variability): 3.32
## R^2 (amount of heterogeneity accounted for): 41.35%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 25.3747, p-val = 0.0080
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.6370, p-val = 0.0313
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0208 0.0125 -1.6644 0.0960 -0.0452 0.0037 .
## continentEurope 0.0290 0.0135 2.1534 0.0313 0.0026 0.0553 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.9064 -19.8127 -13.8127 -12.6190 -10.3841
##
## tau^2 (estimated amount of residual heterogeneity): 0.0086 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0927
## I^2 (residual heterogeneity / unaccounted variability): 92.63%
## H^2 (unaccounted variability / sampling variability): 13.58
## R^2 (amount of heterogeneity accounted for): 26.92%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 184.0555, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.8997, p-val = 0.0269
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0700 0.0930 -0.7529 0.4515 -0.2523 0.1123
## continentEurope -0.2151 0.0972 -2.2135 0.0269 -0.4056 -0.0246 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.7695 -23.5390 -17.5390 -16.3453 -14.1104
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0021)
## tau (square root of estimated tau^2 value): 0.0548
## I^2 (residual heterogeneity / unaccounted variability): 95.20%
## H^2 (unaccounted variability / sampling variability): 20.82
## R^2 (amount of heterogeneity accounted for): 24.01%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 80.5192, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.8400, p-val = 0.0919
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1652 0.1681 0.9828 0.3257 -0.1642 0.4945
## mean.age -0.0047 0.0028 -1.6852 0.0919 -0.0102 0.0008 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.0700 -54.1399 -48.1399 -46.9462 -44.7114
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0171
## I^2 (residual heterogeneity / unaccounted variability): 90.82%
## H^2 (unaccounted variability / sampling variability): 10.89
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 216.8083, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2241, p-val = 0.6360
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0197 0.0516 -0.3820 0.7024 -0.1209 0.0814
## mean.age 0.0004 0.0008 0.4733 0.6360 -0.0013 0.0021
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.9280 -15.8560 -9.8560 -8.6623 -6.4275
##
## tau^2 (estimated amount of residual heterogeneity): 0.0129 (SE = 0.0059)
## tau (square root of estimated tau^2 value): 0.1138
## I^2 (residual heterogeneity / unaccounted variability): 95.78%
## H^2 (unaccounted variability / sampling variability): 23.71
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 515.3742, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0007, p-val = 0.9787
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2771 0.3255 -0.8514 0.3946 -0.9150 0.3608
## mean.age 0.0001 0.0052 0.0267 0.9787 -0.0101 0.0104
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.9257 -21.8514 -15.8514 -14.6577 -12.4228
##
## tau^2 (estimated amount of residual heterogeneity): 0.0044 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0.0665
## I^2 (residual heterogeneity / unaccounted variability): 92.75%
## H^2 (unaccounted variability / sampling variability): 13.79
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 73.6877, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7831, p-val = 0.3762
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1937 0.0866 -2.2361 0.0253 -0.3634 -0.0239 *
## scale2 0.0158 0.0179 0.8849 0.3762 -0.0192 0.0509
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.6087 -55.2174 -49.2174 -48.0238 -45.7889
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0150
## I^2 (residual heterogeneity / unaccounted variability): 79.59%
## H^2 (unaccounted variability / sampling variability): 4.90
## R^2 (amount of heterogeneity accounted for): 14.35%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 47.3630, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.6255, p-val = 0.2023
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0206 0.0203 -1.0142 0.3105 -0.0604 0.0192
## scale2 0.0053 0.0042 1.2750 0.2023 -0.0028 0.0135
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.1025 -18.2050 -12.2050 -11.0113 -8.7765
##
## tau^2 (estimated amount of residual heterogeneity): 0.0099 (SE = 0.0046)
## tau (square root of estimated tau^2 value): 0.0996
## I^2 (residual heterogeneity / unaccounted variability): 95.17%
## H^2 (unaccounted variability / sampling variability): 20.69
## R^2 (amount of heterogeneity accounted for): 15.60%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 306.0790, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.7118, p-val = 0.0996
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0744 0.1208 -0.6158 0.5380 -0.3111 0.1624
## scale2 -0.0431 0.0262 -1.6468 0.0996 -0.0943 0.0082 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2391 -6.4783 -2.4783 -5.0920 9.5217
##
## tau^2 (estimated amount of total heterogeneity): 0.0023 (SE = 0.0023)
## tau (square root of estimated tau^2 value): 0.0476
## I^2 (total heterogeneity / total variability): 98.16%
## H^2 (total variability / sampling variability): 54.25
##
## Test for Heterogeneity:
## Q(df = 2) = 151.9451, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4688 0.0278 16.8697 <.0001 0.4143 0.5232 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2832 -2.5664 3.4336 -2.5664 27.4336
##
## tau^2 (estimated amount of residual heterogeneity): 0.0045 (SE = 0.0064)
## tau (square root of estimated tau^2 value): 0.0668
## I^2 (residual heterogeneity / unaccounted variability): 99.28%
## H^2 (unaccounted variability / sampling variability): 138.41
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 138.4144, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0112, p-val = 0.9157
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4628 0.0675 6.8542 <.0001 0.3304 0.5951 ***
## continentEurope 0.0087 0.0825 0.1058 0.9157 -0.1530 0.1704
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3288 -2.6576 3.3424 -2.6576 27.3424
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0058)
## tau (square root of estimated tau^2 value): 0.0638
## I^2 (residual heterogeneity / unaccounted variability): 99.05%
## H^2 (unaccounted variability / sampling variability): 105.13
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 105.1267, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1096, p-val = 0.7406
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5582 0.2732 2.0435 0.0410 0.0228 1.0937 *
## mean.age -0.0016 0.0048 -0.3311 0.7406 -0.0110 0.0078
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.8111 -3.6222 2.3778 -3.6222 26.3778
##
## tau^2 (estimated amount of residual heterogeneity): 0.0015 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0389
## I^2 (residual heterogeneity / unaccounted variability): 96.61%
## H^2 (unaccounted variability / sampling variability): 29.47
## R^2 (amount of heterogeneity accounted for): 33.24%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 29.4684, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9478, p-val = 0.1628
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3056 0.1193 2.5625 0.0104 0.0719 0.5393 *
## scale1 0.0169 0.0121 1.3956 0.1628 -0.0068 0.0406
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5828 -7.1656 -3.1656 -5.7793 8.8344
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0397
## I^2 (total heterogeneity / total variability): 96.50%
## H^2 (total variability / sampling variability): 28.59
##
## Test for Heterogeneity:
## Q(df = 2) = 79.5694, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1674 0.0235 -7.1081 <.0001 -0.2135 -0.1212 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5967 -3.1933 2.8067 -3.1933 26.8067
##
## tau^2 (estimated amount of residual heterogeneity): 0.0024 (SE = 0.0034)
## tau (square root of estimated tau^2 value): 0.0486
## I^2 (residual heterogeneity / unaccounted variability): 98.46%
## H^2 (unaccounted variability / sampling variability): 64.77
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 64.7722, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3148, p-val = 0.5747
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1436 0.0506 -2.8352 0.0046 -0.2428 -0.0443 **
## continentEurope -0.0344 0.0614 -0.5611 0.5747 -0.1547 0.0858
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7508 -3.5015 2.4985 -3.5015 26.4985
##
## tau^2 (estimated amount of residual heterogeneity): 0.0017 (SE = 0.0025)
## tau (square root of estimated tau^2 value): 0.0414
## I^2 (residual heterogeneity / unaccounted variability): 97.31%
## H^2 (unaccounted variability / sampling variability): 37.18
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 37.1791, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7964, p-val = 0.3722
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3282 0.1820 -1.8036 0.0713 -0.6849 0.0285 .
## mean.age 0.0029 0.0032 0.8924 0.3722 -0.0034 0.0092
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6048 -3.2096 2.7904 -3.2096 26.7904
##
## tau^2 (estimated amount of residual heterogeneity): 0.0023 (SE = 0.0033)
## tau (square root of estimated tau^2 value): 0.0475
## I^2 (residual heterogeneity / unaccounted variability): 95.49%
## H^2 (unaccounted variability / sampling variability): 22.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 22.1883, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3765, p-val = 0.5395
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0795 0.1454 -0.5469 0.5845 -0.3645 0.2055
## scale1 -0.0091 0.0148 -0.6136 0.5395 -0.0381 0.0199
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.4996 -6.9992 -2.9992 -5.6129 9.0008
##
## tau^2 (estimated amount of total heterogeneity): 0.0017 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0415
## I^2 (total heterogeneity / total variability): 96.82%
## H^2 (total variability / sampling variability): 31.41
##
## Test for Heterogeneity:
## Q(df = 2) = 91.5192, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1648 0.0246 -6.7105 <.0001 -0.2130 -0.1167 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5080 -3.0160 2.9840 -3.0160 26.9840
##
## tau^2 (estimated amount of residual heterogeneity): 0.0028 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0532
## I^2 (residual heterogeneity / unaccounted variability): 98.72%
## H^2 (unaccounted variability / sampling variability): 78.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 77.9964, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1987, p-val = 0.6558
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1443 0.0550 -2.6214 0.0088 -0.2522 -0.0364 **
## continentEurope -0.0298 0.0668 -0.4457 0.6558 -0.1607 0.1012
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6332 -3.2665 2.7335 -3.2665 26.7335
##
## tau^2 (estimated amount of residual heterogeneity): 0.0022 (SE = 0.0032)
## tau (square root of estimated tau^2 value): 0.0468
## I^2 (residual heterogeneity / unaccounted variability): 97.89%
## H^2 (unaccounted variability / sampling variability): 47.43
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 47.4250, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5459, p-val = 0.4600
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3139 0.2039 -1.5397 0.1236 -0.7136 0.0857
## mean.age 0.0027 0.0036 0.7388 0.4600 -0.0044 0.0097
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6255 -3.2510 2.7490 -3.2510 26.7490
##
## tau^2 (estimated amount of residual heterogeneity): 0.0022 (SE = 0.0032)
## tau (square root of estimated tau^2 value): 0.0465
## I^2 (residual heterogeneity / unaccounted variability): 95.33%
## H^2 (unaccounted variability / sampling variability): 21.43
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 21.4300, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5626, p-val = 0.4532
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0599 0.1424 -0.4206 0.6741 -0.3389 0.2192
## scale1 -0.0109 0.0145 -0.7501 0.4532 -0.0392 0.0175
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6536 -7.3073 -3.3073 -5.9210 8.6927
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0384
## I^2 (total heterogeneity / total variability): 96.43%
## H^2 (total variability / sampling variability): 28.02
##
## Test for Heterogeneity:
## Q(df = 2) = 82.9062, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1703 0.0228 -7.4715 <.0001 -0.2149 -0.1256 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7562 -5.5124 -1.5124 -4.1261 10.4876
##
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0556
## I^2 (total heterogeneity / total variability): 88.03%
## H^2 (total variability / sampling variability): 8.35
##
## Test for Heterogeneity:
## Q(df = 2) = 12.5156, p-val = 0.0019
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3731 0.0344 -10.8491 <.0001 -0.4404 -0.3057 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5614 -3.1228 2.8772 -3.1228 26.8772
##
## tau^2 (estimated amount of residual heterogeneity): 0.0025 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0504
## I^2 (residual heterogeneity / unaccounted variability): 98.61%
## H^2 (unaccounted variability / sampling variability): 72.15
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 72.1466, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1397, p-val = 0.7086
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1538 0.0522 -2.9450 0.0032 -0.2562 -0.0515 **
## continentEurope -0.0237 0.0634 -0.3738 0.7086 -0.1479 0.1005
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1419 -4.2838 1.7162 -4.2838 25.7162
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0207
## I^2 (residual heterogeneity / unaccounted variability): 53.32%
## H^2 (unaccounted variability / sampling variability): 2.14
## R^2 (amount of heterogeneity accounted for): 86.06%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.1424, p-val = 0.1433
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.3646, p-val = 0.0116
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4421 0.0331 -13.3624 <.0001 -0.5070 -0.3773 ***
## continentEurope 0.0967 0.0383 2.5228 0.0116 0.0216 0.1718 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6692 -3.3384 2.6616 -3.3384 26.6616
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0029)
## tau (square root of estimated tau^2 value): 0.0451
## I^2 (residual heterogeneity / unaccounted variability): 97.81%
## H^2 (unaccounted variability / sampling variability): 45.58
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 45.5752, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4202, p-val = 0.5168
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2964 0.1967 -1.5069 0.1318 -0.6818 0.0891
## mean.age 0.0023 0.0035 0.6482 0.5168 -0.0046 0.0091
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6730 -3.3461 2.6539 -3.3461 26.6539
##
## tau^2 (estimated amount of residual heterogeneity): 0.0016 (SE = 0.0029)
## tau (square root of estimated tau^2 value): 0.0403
## I^2 (residual heterogeneity / unaccounted variability): 78.80%
## H^2 (unaccounted variability / sampling variability): 4.72
## R^2 (amount of heterogeneity accounted for): 47.39%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.7169, p-val = 0.0299
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.4489, p-val = 0.1176
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0721 0.1937 -0.3723 0.7096 -0.4517 0.3075
## mean.age -0.0054 0.0034 -1.5649 0.1176 -0.0121 0.0014
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7531 -3.5062 2.4938 -3.5062 26.4938
##
## tau^2 (estimated amount of residual heterogeneity): 0.0017 (SE = 0.0025)
## tau (square root of estimated tau^2 value): 0.0407
## I^2 (residual heterogeneity / unaccounted variability): 94.33%
## H^2 (unaccounted variability / sampling variability): 17.64
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 17.6441, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7341, p-val = 0.3915
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0650 0.1251 -0.5191 0.6037 -0.3102 0.1803
## scale1 -0.0109 0.0127 -0.8568 0.3915 -0.0359 0.0140
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4286 -2.8572 3.1428 -2.8572 27.1428
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0048)
## tau (square root of estimated tau^2 value): 0.0543
## I^2 (residual heterogeneity / unaccounted variability): 87.74%
## H^2 (unaccounted variability / sampling variability): 8.16
## R^2 (amount of heterogeneity accounted for): 4.49%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.1573, p-val = 0.0043
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1699, p-val = 0.2794
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1832 0.1787 -1.0256 0.3051 -0.5334 0.1669
## scale1 -0.0195 0.0181 -1.0816 0.2794 -0.0549 0.0159
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.3138 -6.6277 -2.6277 -5.2414 9.3723
##
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0443
## I^2 (total heterogeneity / total variability): 94.60%
## H^2 (total variability / sampling variability): 18.51
##
## Test for Heterogeneity:
## Q(df = 2) = 42.6115, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1838 0.0268 -6.8594 <.0001 -0.2363 -0.1313 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.2999 -8.5998 -4.5998 -7.2135 7.4002
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 2) = 1.5405, p-val = 0.4629
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3629 0.0111 -32.5617 <.0001 -0.3847 -0.3410 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.0570 -8.1139 -4.1139 -6.7276 7.8861
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0190
## I^2 (total heterogeneity / total variability): 62.05%
## H^2 (total variability / sampling variability): 2.64
##
## Test for Heterogeneity:
## Q(df = 2) = 4.6848, p-val = 0.0961
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0331 0.0142 2.3373 0.0194 0.0053 0.0608 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6478 -3.2956 2.7044 -3.2956 26.7044
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0031)
## tau (square root of estimated tau^2 value): 0.0458
## I^2 (residual heterogeneity / unaccounted variability): 96.80%
## H^2 (unaccounted variability / sampling variability): 31.22
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 31.2172, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8513, p-val = 0.3562
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1444 0.0507 -2.8492 0.0044 -0.2438 -0.0451 **
## continentEurope -0.0558 0.0604 -0.9226 0.3562 -0.1742 0.0627
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.5625 -5.1250 0.8750 -5.1250 24.8750
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.8746, p-val = 0.3497
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6659, p-val = 0.4145
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4115 0.0606 -6.7919 <.0001 -0.5302 -0.2927 ***
## continentEurope 0.0503 0.0616 0.8160 0.4145 -0.0705 0.1711
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9403, p-val = 0.3322
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.7445, p-val = 0.0530
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0156 0.0279 -0.5595 0.5758 -0.0702 0.0390
## continentEurope 0.0554 0.0286 1.9351 0.0530 -0.0007 0.1115 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9055 -3.8110 2.1890 -3.8110 26.1890
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0347
## I^2 (residual heterogeneity / unaccounted variability): 92.95%
## H^2 (unaccounted variability / sampling variability): 14.18
## R^2 (amount of heterogeneity accounted for): 38.78%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.1799, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.0710, p-val = 0.1501
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4170 0.1626 -2.5654 0.0103 -0.7357 -0.0984 *
## mean.age 0.0042 0.0029 1.4391 0.1501 -0.0015 0.0099
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1499 -4.2999 1.7001 -4.2999 25.7001
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0157
## I^2 (residual heterogeneity / unaccounted variability): 31.02%
## H^2 (unaccounted variability / sampling variability): 1.45
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.4497, p-val = 0.2286
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2150, p-val = 0.6429
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2770 0.1833 -1.5113 0.1307 -0.6363 0.0822
## mean.age -0.0016 0.0036 -0.4637 0.6429 -0.0086 0.0053
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0171
## I^2 (residual heterogeneity / unaccounted variability): 61.44%
## H^2 (unaccounted variability / sampling variability): 2.59
## R^2 (amount of heterogeneity accounted for): 19.04%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.5933, p-val = 0.1073
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.0246, p-val = 0.1548
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1919 0.1119 1.7139 0.0865 -0.0275 0.4112 .
## mean.age -0.0030 0.0021 -1.4229 0.1548 -0.0071 0.0011
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3573 -2.7146 3.2854 -2.7146 27.2854
##
## tau^2 (estimated amount of residual heterogeneity): 0.0036 (SE = 0.0055)
## tau (square root of estimated tau^2 value): 0.0602
## I^2 (residual heterogeneity / unaccounted variability): 93.59%
## H^2 (unaccounted variability / sampling variability): 15.59
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 15.5935, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1044, p-val = 0.7466
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1240 0.1849 -0.6706 0.5025 -0.4865 0.2385
## scale1 -0.0061 0.0188 -0.3231 0.7466 -0.0430 0.0308
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9466 -3.8933 2.1067 -3.8933 26.1067
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.5265, p-val = 0.4681
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0140, p-val = 0.3139
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2910 0.0723 -4.0269 <.0001 -0.4326 -0.1494 ***
## scale1 -0.0070 0.0070 -1.0070 0.3139 -0.0207 0.0067
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.8771 -3.7542 2.2458 -3.7542 26.2458
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0019)
## tau (square root of estimated tau^2 value): 0.0309
## I^2 (residual heterogeneity / unaccounted variability): 69.74%
## H^2 (unaccounted variability / sampling variability): 3.31
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 3.3052, p-val = 0.0691
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6957, p-val = 0.4042
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1155 0.1046 1.1045 0.2694 -0.0895 0.3206
## scale1 -0.0089 0.0107 -0.8341 0.4042 -0.0299 0.0120
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7913 -5.5827 -1.5827 -4.1964 10.4173
##
## tau^2 (estimated amount of total heterogeneity): 0.0035 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0594
## I^2 (total heterogeneity / total variability): 98.37%
## H^2 (total variability / sampling variability): 61.20
##
## Test for Heterogeneity:
## Q(df = 2) = 143.2600, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4097 0.0347 11.8206 <.0001 0.3417 0.4776 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1260 -2.2520 3.7480 -2.2520 27.7480
##
## tau^2 (estimated amount of residual heterogeneity): 0.0061 (SE = 0.0087)
## tau (square root of estimated tau^2 value): 0.0782
## I^2 (residual heterogeneity / unaccounted variability): 99.29%
## H^2 (unaccounted variability / sampling variability): 141.21
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 141.2076, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1596, p-val = 0.6895
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4355 0.0790 5.5138 <.0001 0.2807 0.5903 ***
## continentEurope -0.0386 0.0965 -0.3995 0.6895 -0.2278 0.1506
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.0653 -2.1306 3.8694 -2.1306 27.8694
##
## tau^2 (estimated amount of residual heterogeneity): 0.0069 (SE = 0.0098)
## tau (square root of estimated tau^2 value): 0.0831
## I^2 (residual heterogeneity / unaccounted variability): 99.28%
## H^2 (unaccounted variability / sampling variability): 138.93
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 138.9308, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0282, p-val = 0.8666
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3505 0.3555 0.9860 0.3241 -0.3462 1.0472
## mean.age 0.0011 0.0063 0.1680 0.8666 -0.0112 0.0133
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0155 -6.0310 -0.0310 -6.0310 23.9690
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0081
## I^2 (residual heterogeneity / unaccounted variability): 46.85%
## H^2 (unaccounted variability / sampling variability): 1.88
## R^2 (amount of heterogeneity accounted for): 98.13%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.8815, p-val = 0.1702
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 58.9630, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1567 0.0339 4.6265 <.0001 0.0903 0.2231 ***
## scale1 0.0264 0.0034 7.6787 <.0001 0.0196 0.0331 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.0642 -10.1284 -6.1284 -8.7421 5.8716
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0180
## I^2 (total heterogeneity / total variability): 83.94%
## H^2 (total variability / sampling variability): 6.23
##
## Test for Heterogeneity:
## Q(df = 2) = 16.3123, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1747 0.0117 -14.9541 <.0001 -0.1976 -0.1518 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2959 -4.5917 1.4083 -4.5917 25.4083
##
## tau^2 (estimated amount of residual heterogeneity): 0.0006 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0235
## I^2 (residual heterogeneity / unaccounted variability): 93.33%
## H^2 (unaccounted variability / sampling variability): 14.99
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.9881, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0666, p-val = 0.7964
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1680 0.0279 -6.0127 <.0001 -0.2228 -0.1133 ***
## continentEurope -0.0085 0.0328 -0.2581 0.7964 -0.0728 0.0559
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3771 -4.7541 1.2459 -4.7541 25.2459
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0213
## I^2 (residual heterogeneity / unaccounted variability): 89.95%
## H^2 (unaccounted variability / sampling variability): 9.95
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.9494, p-val = 0.0016
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2827, p-val = 0.5950
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2283 0.1024 -2.2301 0.0257 -0.4290 -0.0277 *
## mean.age 0.0010 0.0018 0.5317 0.5950 -0.0026 0.0046
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6155 -5.2309 0.7691 -5.2309 24.7691
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0138
## I^2 (residual heterogeneity / unaccounted variability): 61.08%
## H^2 (unaccounted variability / sampling variability): 2.57
## R^2 (amount of heterogeneity accounted for): 41.09%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.5691, p-val = 0.1090
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.7407, p-val = 0.1870
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1129 0.0484 -2.3303 0.0198 -0.2078 -0.0179 *
## scale1 -0.0065 0.0050 -1.3194 0.1870 -0.0162 0.0032
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.1853 -10.3707 -6.3707 -8.9844 5.6293
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0167
## I^2 (total heterogeneity / total variability): 81.87%
## H^2 (total variability / sampling variability): 5.52
##
## Test for Heterogeneity:
## Q(df = 2) = 14.2366, p-val = 0.0008
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1747 0.0110 -15.8989 <.0001 -0.1963 -0.1532 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3642 -4.7284 1.2716 -4.7284 25.2716
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0219
## I^2 (residual heterogeneity / unaccounted variability): 92.36%
## H^2 (unaccounted variability / sampling variability): 13.08
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.0828, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0655, p-val = 0.7980
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1684 0.0265 -6.3510 <.0001 -0.2203 -0.1164 ***
## continentEurope -0.0079 0.0310 -0.2559 0.7980 -0.0687 0.0528
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4448 -4.8897 1.1103 -4.8897 25.1103
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0197
## I^2 (residual heterogeneity / unaccounted variability): 88.52%
## H^2 (unaccounted variability / sampling variability): 8.71
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.7102, p-val = 0.0032
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2838, p-val = 0.5942
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2253 0.0964 -2.3363 0.0195 -0.4143 -0.0363 *
## mean.age 0.0009 0.0017 0.5328 0.5942 -0.0025 0.0043
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6867 -5.3734 0.6266 -5.3734 24.6266
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0123
## I^2 (residual heterogeneity / unaccounted variability): 55.59%
## H^2 (unaccounted variability / sampling variability): 2.25
## R^2 (amount of heterogeneity accounted for): 46.03%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2516, p-val = 0.1335
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.8955, p-val = 0.1686
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1158 0.0444 -2.6084 0.0091 -0.2027 -0.0288 **
## scale1 -0.0062 0.0045 -1.3768 0.1686 -0.0151 0.0026
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.3576 -10.7151 -6.7151 -9.3288 5.2849
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0151
## I^2 (total heterogeneity / total variability): 79.07%
## H^2 (total variability / sampling variability): 4.78
##
## Test for Heterogeneity:
## Q(df = 2) = 12.0238, p-val = 0.0024
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1793 0.0101 -17.7267 <.0001 -0.1992 -0.1595 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7595 -5.5189 -1.5189 -4.1326 10.4811
##
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0560
## I^2 (total heterogeneity / total variability): 87.81%
## H^2 (total variability / sampling variability): 8.21
##
## Test for Heterogeneity:
## Q(df = 2) = 14.3779, p-val = 0.0008
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2714 0.0347 -7.8218 <.0001 -0.3394 -0.2034 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4320 -4.8639 1.1361 -4.8639 25.1361
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0203
## I^2 (residual heterogeneity / unaccounted variability): 91.41%
## H^2 (unaccounted variability / sampling variability): 11.65
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 11.6481, p-val = 0.0006
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0018, p-val = 0.9660
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1780 0.0251 -7.0862 <.0001 -0.2272 -0.1287 ***
## continentEurope -0.0012 0.0293 -0.0427 0.9660 -0.0586 0.0561
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2818 -2.5636 3.4364 -2.5636 27.4364
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0064)
## tau (square root of estimated tau^2 value): 0.0644
## I^2 (residual heterogeneity / unaccounted variability): 91.90%
## H^2 (unaccounted variability / sampling variability): 12.35
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.3468, p-val = 0.0004
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6069, p-val = 0.4360
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3165 0.0700 -4.5214 <.0001 -0.4536 -0.1793 ***
## continentEurope 0.0659 0.0846 0.7790 0.4360 -0.0999 0.2316
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4590 -4.9179 1.0821 -4.9179 25.0821
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0195
## I^2 (residual heterogeneity / unaccounted variability): 88.42%
## H^2 (unaccounted variability / sampling variability): 8.64
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.6391, p-val = 0.0033
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0849, p-val = 0.7708
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2064 0.0950 -2.1725 0.0298 -0.3926 -0.0202 *
## mean.age 0.0005 0.0017 0.2913 0.7708 -0.0029 0.0039
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1402 -2.2803 3.7197 -2.2803 27.7197
##
## tau^2 (estimated amount of residual heterogeneity): 0.0056 (SE = 0.0085)
## tau (square root of estimated tau^2 value): 0.0746
## I^2 (residual heterogeneity / unaccounted variability): 92.92%
## H^2 (unaccounted variability / sampling variability): 14.12
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.1244, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2355, p-val = 0.6275
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1120 0.3313 -0.3381 0.7353 -0.7613 0.5373
## mean.age -0.0028 0.0059 -0.4853 0.6275 -0.0143 0.0086
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0928 -6.1857 -0.1857 -6.1857 23.8143
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0019
## I^2 (residual heterogeneity / unaccounted variability): 3.10%
## H^2 (unaccounted variability / sampling variability): 1.03
## R^2 (amount of heterogeneity accounted for): 98.36%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.0320, p-val = 0.3097
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 9.9312, p-val = 0.0016
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1130 0.0234 -4.8226 <.0001 -0.1590 -0.0671 ***
## scale1 -0.0072 0.0023 -3.1514 0.0016 -0.0117 -0.0027 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6951 -5.3901 0.6099 -5.3901 24.6099
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.4535, p-val = 0.5007
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 13.9245, p-val = 0.0002
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0283 0.0684 -0.4139 0.6790 -0.1623 0.1057
## scale1 -0.0247 0.0066 -3.7316 0.0002 -0.0376 -0.0117 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.7723 -11.5446 -7.5446 -10.1583 4.4554
##
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0005
## I^2 (total heterogeneity / total variability): 0.21%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 2) = 2.1896, p-val = 0.3346
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1834 0.0050 -36.7338 <.0001 -0.1932 -0.1736 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8349 -5.6697 -1.6697 -4.2834 10.3303
##
## tau^2 (estimated amount of total heterogeneity): 0.0028 (SE = 0.0039)
## tau (square root of estimated tau^2 value): 0.0533
## I^2 (total heterogeneity / total variability): 81.59%
## H^2 (total variability / sampling variability): 5.43
##
## Test for Heterogeneity:
## Q(df = 2) = 13.8097, p-val = 0.0010
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2494 0.0360 -6.9232 <.0001 -0.3200 -0.1788 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6823 -7.3645 -3.3645 -5.9782 8.6355
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0014)
## tau (square root of estimated tau^2 value): 0.0319
## I^2 (total heterogeneity / total variability): 80.61%
## H^2 (total variability / sampling variability): 5.16
##
## Test for Heterogeneity:
## Q(df = 2) = 9.3161, p-val = 0.0095
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0030 0.0211 -0.1422 0.8869 -0.0444 0.0384
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6864 -7.3729 -1.3729 -7.3729 22.6271
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.3067, p-val = 0.5797
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.8829, p-val = 0.1700
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1525 0.0231 -6.5998 <.0001 -0.1978 -0.1072 ***
## continentEurope -0.0325 0.0237 -1.3722 0.1700 -0.0788 0.0139
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2385 -2.4770 3.5230 -2.4770 27.5230
##
## tau^2 (estimated amount of residual heterogeneity): 0.0046 (SE = 0.0070)
## tau (square root of estimated tau^2 value): 0.0675
## I^2 (residual heterogeneity / unaccounted variability): 92.53%
## H^2 (unaccounted variability / sampling variability): 13.39
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.3916, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0275, p-val = 0.8683
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2344 0.0926 -2.5302 0.0114 -0.4160 -0.0528 *
## continentEurope -0.0174 0.1051 -0.1659 0.8683 -0.2233 0.1885
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0322
## I^2 (residual heterogeneity / unaccounted variability): 86.94%
## H^2 (unaccounted variability / sampling variability): 7.66
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.6565, p-val = 0.0057
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0764, p-val = 0.2995
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0430 0.0440 -0.9770 0.3286 -0.1293 0.0433
## continentEurope 0.0522 0.0503 1.0375 0.2995 -0.0464 0.1508
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7003 -7.4006 -1.4006 -7.4006 22.5994
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0044, p-val = 0.9471
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.1852, p-val = 0.1393
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2779 0.0641 -4.3366 <.0001 -0.4034 -0.1523 ***
## mean.age 0.0019 0.0013 1.4782 0.1393 -0.0006 0.0043
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3012 -2.6023 3.3977 -2.6023 27.3977
##
## tau^2 (estimated amount of residual heterogeneity): 0.0038 (SE = 0.0061)
## tau (square root of estimated tau^2 value): 0.0618
## I^2 (residual heterogeneity / unaccounted variability): 87.94%
## H^2 (unaccounted variability / sampling variability): 8.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.2948, p-val = 0.0040
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2076, p-val = 0.6487
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3963 0.3271 -1.2117 0.2256 -1.0373 0.2447
## mean.age 0.0027 0.0060 0.4556 0.6487 -0.0090 0.0144
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0017 (SE = 0.0026)
## tau (square root of estimated tau^2 value): 0.0407
## I^2 (residual heterogeneity / unaccounted variability): 89.24%
## H^2 (unaccounted variability / sampling variability): 9.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.2935, p-val = 0.0023
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4640, p-val = 0.4957
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1293 0.1974 0.6552 0.5123 -0.2575 0.5162
## mean.age -0.0024 0.0036 -0.6812 0.4957 -0.0094 0.0045
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3089 -4.6179 1.3821 -4.6179 25.3821
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0172
## I^2 (residual heterogeneity / unaccounted variability): 50.92%
## H^2 (unaccounted variability / sampling variability): 2.04
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.0375, p-val = 0.1535
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0134, p-val = 0.9080
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1849 0.0617 -2.9980 0.0027 -0.3058 -0.0640 **
## scale1 0.0007 0.0063 0.1156 0.9080 -0.0117 0.0132
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6602 -3.3204 2.6796 -3.3204 26.6796
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0030)
## tau (square root of estimated tau^2 value): 0.0037
## I^2 (residual heterogeneity / unaccounted variability): 0.66%
## H^2 (unaccounted variability / sampling variability): 1.01
## R^2 (amount of heterogeneity accounted for): 99.51%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.0066, p-val = 0.3157
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 12.2996, p-val = 0.0005
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0317 0.0700 -0.4535 0.6502 -0.1689 0.1054
## scale1 -0.0241 0.0069 -3.5071 0.0005 -0.0375 -0.0106 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4443 -4.8886 1.1114 -4.8886 25.1114
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9106, p-val = 0.3400
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.4055, p-val = 0.0037
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1244 0.0453 2.7456 0.0060 0.0356 0.2132 **
## scale1 -0.0127 0.0044 -2.8992 0.0037 -0.0213 -0.0041 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2938 -6.5876 -2.5876 -5.2013 9.4124
##
## tau^2 (estimated amount of total heterogeneity): 0.0021 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0459
## I^2 (total heterogeneity / total variability): 97.34%
## H^2 (total variability / sampling variability): 37.57
##
## Test for Heterogeneity:
## Q(df = 2) = 86.4426, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3790 0.0270 14.0430 <.0001 0.3261 0.4319 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4510 -2.9020 3.0980 -2.9020 27.0980
##
## tau^2 (estimated amount of residual heterogeneity): 0.0032 (SE = 0.0045)
## tau (square root of estimated tau^2 value): 0.0564
## I^2 (residual heterogeneity / unaccounted variability): 98.81%
## H^2 (unaccounted variability / sampling variability): 84.20
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 84.1991, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3303, p-val = 0.5655
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4064 0.0579 7.0201 <.0001 0.2930 0.5199 ***
## continentEurope -0.0405 0.0704 -0.5747 0.5655 -0.1785 0.0976
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3544 -2.7089 3.2911 -2.7089 27.2911
##
## tau^2 (estimated amount of residual heterogeneity): 0.0039 (SE = 0.0055)
## tau (square root of estimated tau^2 value): 0.0621
## I^2 (residual heterogeneity / unaccounted variability): 98.80%
## H^2 (unaccounted variability / sampling variability): 83.27
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 83.2654, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1009, p-val = 0.7507
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2949 0.2675 1.1024 0.2703 -0.2294 0.8193
## mean.age 0.0015 0.0047 0.3177 0.7507 -0.0077 0.0107
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7004 -7.4008 -1.4008 -7.4008 22.5992
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0020, p-val = 0.9647
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 86.4407, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1852 0.0220 8.3995 <.0001 0.1420 0.2284 ***
## scale1 0.0201 0.0022 9.2973 <.0001 0.0158 0.0243 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6147 -7.2294 -3.2294 -5.8431 8.7706
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0392
## I^2 (total heterogeneity / total variability): 96.96%
## H^2 (total variability / sampling variability): 32.93
##
## Test for Heterogeneity:
## Q(df = 2) = 94.4415, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0841 0.0232 -3.6265 0.0003 -0.1295 -0.0386 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5085 -3.0171 2.9829 -3.0171 26.9829
##
## tau^2 (estimated amount of residual heterogeneity): 0.0028 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0532
## I^2 (residual heterogeneity / unaccounted variability): 98.94%
## H^2 (unaccounted variability / sampling variability): 94.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 94.3332, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0694, p-val = 0.7921
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0960 0.0549 -1.7475 0.0805 -0.2037 0.0117 .
## continentEurope 0.0176 0.0667 0.2635 0.7921 -0.1132 0.1483
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4743 -2.9485 3.0515 -2.9485 27.0515
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0043)
## tau (square root of estimated tau^2 value): 0.0550
## I^2 (residual heterogeneity / unaccounted variability): 98.72%
## H^2 (unaccounted variability / sampling variability): 78.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 78.2939, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0017, p-val = 0.9667
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0742 0.2381 -0.3118 0.7552 -0.5408 0.3923
## mean.age -0.0002 0.0042 -0.0417 0.9667 -0.0084 0.0081
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8379 -5.6757 0.3243 -5.6757 24.3243
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0102
## I^2 (residual heterogeneity / unaccounted variability): 51.53%
## H^2 (unaccounted variability / sampling variability): 2.06
## R^2 (amount of heterogeneity accounted for): 93.26%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.0630, p-val = 0.1509
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 20.8789, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0820 0.0374 2.1926 0.0283 0.0087 0.1554 *
## scale1 -0.0175 0.0038 -4.5693 <.0001 -0.0250 -0.0100 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5876 -7.1753 -3.1753 -5.7890 8.8247
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0397
## I^2 (total heterogeneity / total variability): 97.06%
## H^2 (total variability / sampling variability): 33.99
##
## Test for Heterogeneity:
## Q(df = 2) = 97.5929, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0847 0.0235 -3.6052 0.0003 -0.1307 -0.0386 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4936 -2.9871 3.0129 -2.9871 27.0129
##
## tau^2 (estimated amount of residual heterogeneity): 0.0029 (SE = 0.0042)
## tau (square root of estimated tau^2 value): 0.0541
## I^2 (residual heterogeneity / unaccounted variability): 98.97%
## H^2 (unaccounted variability / sampling variability): 97.46
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 97.4647, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0675, p-val = 0.7951
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0966 0.0557 -1.7342 0.0829 -0.2058 0.0126 .
## continentEurope 0.0176 0.0677 0.2597 0.7951 -0.1151 0.1502
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4601 -2.9203 3.0797 -2.9203 27.0797
##
## tau^2 (estimated amount of residual heterogeneity): 0.0031 (SE = 0.0045)
## tau (square root of estimated tau^2 value): 0.0558
## I^2 (residual heterogeneity / unaccounted variability): 98.76%
## H^2 (unaccounted variability / sampling variability): 80.83
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 80.8303, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0015, p-val = 0.9695
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0755 0.2413 -0.3130 0.7543 -0.5486 0.3975
## mean.age -0.0002 0.0043 -0.0383 0.9695 -0.0085 0.0082
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8094 -5.6189 0.3811 -5.6189 24.3811
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0108
## I^2 (residual heterogeneity / unaccounted variability): 54.67%
## H^2 (unaccounted variability / sampling variability): 2.21
## R^2 (amount of heterogeneity accounted for): 92.64%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2059, p-val = 0.1375
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 19.6293, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0832 0.0390 2.1360 0.0327 0.0069 0.1596 *
## scale1 -0.0177 0.0040 -4.4305 <.0001 -0.0255 -0.0098 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6265 -7.2531 -3.2531 -5.8668 8.7469
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0389
## I^2 (total heterogeneity / total variability): 96.99%
## H^2 (total variability / sampling variability): 33.21
##
## Test for Heterogeneity:
## Q(df = 2) = 92.6322, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0871 0.0230 -3.7846 0.0002 -0.1322 -0.0420 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2291 -6.4582 -2.4582 -5.0719 9.5418
##
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0443
## I^2 (total heterogeneity / total variability): 84.02%
## H^2 (total variability / sampling variability): 6.26
##
## Test for Heterogeneity:
## Q(df = 2) = 13.8450, p-val = 0.0010
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2503 0.0281 -8.9043 <.0001 -0.3054 -0.1952 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5270 -3.0541 2.9459 -3.0541 26.9459
##
## tau^2 (estimated amount of residual heterogeneity): 0.0027 (SE = 0.0039)
## tau (square root of estimated tau^2 value): 0.0523
## I^2 (residual heterogeneity / unaccounted variability): 98.92%
## H^2 (unaccounted variability / sampling variability): 92.62
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 92.6211, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0983, p-val = 0.7538
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1011 0.0539 -1.8741 0.0609 -0.2068 0.0046 .
## continentEurope 0.0205 0.0655 0.3136 0.7538 -0.1078 0.1489
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2976 -2.5951 3.4049 -2.5951 27.4049
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0062)
## tau (square root of estimated tau^2 value): 0.0636
## I^2 (residual heterogeneity / unaccounted variability): 92.69%
## H^2 (unaccounted variability / sampling variability): 13.68
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.6766, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0228, p-val = 0.8801
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2578 0.0684 -3.7680 0.0002 -0.3919 -0.1237 ***
## continentEurope 0.0125 0.0828 0.1509 0.8801 -0.1499 0.1749
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4824 -2.9648 3.0352 -2.9648 27.0352
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0043)
## tau (square root of estimated tau^2 value): 0.0546
## I^2 (residual heterogeneity / unaccounted variability): 98.73%
## H^2 (unaccounted variability / sampling variability): 78.55
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 78.5499, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0078, p-val = 0.9296
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0665 0.2361 -0.2816 0.7783 -0.5292 0.3962
## mean.age -0.0004 0.0042 -0.0884 0.9296 -0.0085 0.0078
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2828 -2.5655 3.4345 -2.5655 27.4345
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0064)
## tau (square root of estimated tau^2 value): 0.0643
## I^2 (residual heterogeneity / unaccounted variability): 91.77%
## H^2 (unaccounted variability / sampling variability): 12.15
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.1479, p-val = 0.0005
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0045, p-val = 0.9467
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2683 0.2871 -0.9345 0.3501 -0.8309 0.2944
## mean.age 0.0003 0.0051 0.0668 0.9467 -0.0096 0.0103
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0391 -6.0782 -0.0782 -6.0782 23.9218
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0063
## I^2 (residual heterogeneity / unaccounted variability): 29.89%
## H^2 (unaccounted variability / sampling variability): 1.43
## R^2 (amount of heterogeneity accounted for): 97.35%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.4264, p-val = 0.2323
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 38.9987, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0814 0.0281 2.8905 0.0038 0.0262 0.1365 **
## scale1 -0.0178 0.0028 -6.2449 <.0001 -0.0234 -0.0122 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3580 -4.7159 1.2841 -4.7159 25.2841
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0117
## I^2 (residual heterogeneity / unaccounted variability): 25.98%
## H^2 (unaccounted variability / sampling variability): 1.35
## R^2 (amount of heterogeneity accounted for): 93.07%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.3510, p-val = 0.2451
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.6026, p-val = 0.0034
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0483 0.0735 -0.6572 0.5110 -0.1923 0.0957
## scale1 -0.0212 0.0072 -2.9330 0.0034 -0.0354 -0.0070 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7059 -7.4117 -3.4117 -6.0254 8.5883
##
## tau^2 (estimated amount of total heterogeneity): 0.0014 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0370
## I^2 (total heterogeneity / total variability): 93.41%
## H^2 (total variability / sampling variability): 15.17
##
## Test for Heterogeneity:
## Q(df = 2) = 45.6253, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0958 0.0226 -4.2301 <.0001 -0.1401 -0.0514 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8847 -5.7694 -1.7694 -4.3831 10.2306
##
## tau^2 (estimated amount of total heterogeneity): 0.0026 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0509
## I^2 (total heterogeneity / total variability): 81.98%
## H^2 (total variability / sampling variability): 5.55
##
## Test for Heterogeneity:
## Q(df = 2) = 13.4925, p-val = 0.0012
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2456 0.0343 -7.1626 <.0001 -0.3128 -0.1784 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.9531 -7.9061 -3.9061 -6.5198 8.0939
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0227
## I^2 (total heterogeneity / total variability): 73.06%
## H^2 (total variability / sampling variability): 3.71
##
## Test for Heterogeneity:
## Q(df = 2) = 5.2178, p-val = 0.0736
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0245 0.0158 1.5473 0.1218 -0.0065 0.0554
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5745 -3.1489 2.8511 -3.1489 26.8511
##
## tau^2 (estimated amount of residual heterogeneity): 0.0025 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0495
## I^2 (residual heterogeneity / unaccounted variability): 97.68%
## H^2 (unaccounted variability / sampling variability): 43.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 43.1890, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0509, p-val = 0.8214
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0851 0.0539 -1.5777 0.1146 -0.1907 0.0206
## continentEurope -0.0146 0.0645 -0.2257 0.8214 -0.1410 0.1119
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3471 -2.6943 3.3057 -2.6943 27.3057
##
## tau^2 (estimated amount of residual heterogeneity): 0.0036 (SE = 0.0056)
## tau (square root of estimated tau^2 value): 0.0603
## I^2 (residual heterogeneity / unaccounted variability): 91.75%
## H^2 (unaccounted variability / sampling variability): 12.12
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.1193, p-val = 0.0005
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2574, p-val = 0.6119
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2057 0.0848 -2.4249 0.0153 -0.3719 -0.0394 *
## continentEurope -0.0486 0.0958 -0.5073 0.6119 -0.2363 0.1391
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.4999, p-val = 0.4795
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.7179, p-val = 0.0299
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0262 0.0272 -0.9626 0.3357 -0.0795 0.0271
## continentEurope 0.0606 0.0279 2.1721 0.0299 0.0059 0.1153 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6484 -3.2968 2.7032 -3.2968 26.7032
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0031)
## tau (square root of estimated tau^2 value): 0.0457
## I^2 (residual heterogeneity / unaccounted variability): 96.41%
## H^2 (unaccounted variability / sampling variability): 27.88
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 27.8790, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2292, p-val = 0.6321
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1929 0.2056 -0.9382 0.3481 -0.5958 0.2101
## mean.age 0.0018 0.0037 0.4787 0.6321 -0.0054 0.0089
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5103 -3.0205 2.9795 -3.0205 26.9795
##
## tau^2 (estimated amount of residual heterogeneity): 0.0024 (SE = 0.0040)
## tau (square root of estimated tau^2 value): 0.0489
## I^2 (residual heterogeneity / unaccounted variability): 83.68%
## H^2 (unaccounted variability / sampling variability): 6.13
## R^2 (amount of heterogeneity accounted for): 7.74%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 6.1268, p-val = 0.0133
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7924, p-val = 0.3734
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4901 0.2762 -1.7744 0.0760 -1.0315 0.0513 .
## mean.age 0.0045 0.0051 0.8902 0.3734 -0.0054 0.0145
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0138
## I^2 (residual heterogeneity / unaccounted variability): 55.26%
## H^2 (unaccounted variability / sampling variability): 2.23
## R^2 (amount of heterogeneity accounted for): 62.87%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2349, p-val = 0.1349
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.8308, p-val = 0.0925
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1966 0.1009 1.9486 0.0513 -0.0011 0.3943 .
## mean.age -0.0032 0.0019 -1.6825 0.0925 -0.0069 0.0005 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9326 -3.8653 2.1347 -3.8653 26.1347
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0314
## I^2 (residual heterogeneity / unaccounted variability): 80.51%
## H^2 (unaccounted variability / sampling variability): 5.13
## R^2 (amount of heterogeneity accounted for): 27.85%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 5.1296, p-val = 0.0235
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.5190, p-val = 0.2178
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0245 0.0999 0.2454 0.8061 -0.1713 0.2203
## scale1 -0.0126 0.0102 -1.2325 0.2178 -0.0326 0.0074
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3241 -2.6482 3.3518 -2.6482 27.3518
##
## tau^2 (estimated amount of residual heterogeneity): 0.0023 (SE = 0.0059)
## tau (square root of estimated tau^2 value): 0.0478
## I^2 (residual heterogeneity / unaccounted variability): 55.24%
## H^2 (unaccounted variability / sampling variability): 2.23
## R^2 (amount of heterogeneity accounted for): 11.62%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2340, p-val = 0.1350
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8570, p-val = 0.3546
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0989 0.1625 -0.6085 0.5429 -0.4174 0.2196
## scale1 -0.0156 0.0168 -0.9258 0.3546 -0.0485 0.0174
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7678 -3.5355 2.4645 -3.5355 26.4645
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0362
## I^2 (residual heterogeneity / unaccounted variability): 76.89%
## H^2 (unaccounted variability / sampling variability): 4.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.3264, p-val = 0.0375
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5037, p-val = 0.4779
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1029 0.1177 0.8744 0.3819 -0.1278 0.3337
## scale1 -0.0085 0.0120 -0.7097 0.4779 -0.0321 0.0151
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
4.7. Social risk-taking